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1
Zhang, Chaoyang, Juchen Zhang, Weixi Ji, and Wei Peng. "Data Acquisition Network Configuration and Real-Time Energy Consumption Characteristic Analysis in Intelligent Workshops for Social Manufacturing." Machines 10, no. 10 (October 10, 2022): 923. http://dx.doi.org/10.3390/machines10100923.
Повний текст джерелаАнотація:
To achieve energy-saving production, one critical step is to calculate and analyze the energy consumption and energy efficiency of machining processes. However, considering the complexity and uncertainty of discrete manufacturing job shops, it is a significant challenge to conduct data acquisition and energy consumption data processing of manufacturing systems. Meanwhile, under the growing trend of personalization, social manufacturing is an emerging technical practice that allows prosumers to build individualized services with their partners, which produces new requirements for energy data processing. Thus, a real-time energy consumption characteristic analysis method in intelligent workshops for social manufacturing is established to realize data processing and energy efficiency evaluation automatically. First, an energy-conservation production architecture for intelligent manufacturing processes is introduced, and the configuration of a data acquisition network is described to create a ubiquitous manufacturing environment. Then, an energy consumption characteristic analysis method is proposed based on the process time window. Finally, a case study of coupling-part manufacturing verifies the feasibility and applicability of the proposed method. This method realizes a combination of social manufacturing and real-time energy characteristic analysis. Meanwhile, the energy consumption characteristics provide a decision basis for the energy-saving control of intelligent manufacturing workshops.
2
Jones, Lewis C. R., Nicholas Goffin, Jinglei Ouyang, Nazanin Mirhossein, Jiaji Xiong, Yufeng Li, Lin Li, et al. "Laser specific energy consumption: How do laser systems compare to other manufacturing processes?" Journal of Laser Applications 34, no. 4 (November 2022): 042029. http://dx.doi.org/10.2351/7.0000790.
Повний текст джерелаАнотація:
Laser material interactions are routinely praised for their selective processing and high processing rates. However, this does not guarantee that the total manufacturing system has a low energy intensity compared to conventional manufacturing processes. This paper presents the results of a collaborative UK and China research project to improve the comprehension of the total energy consumption and carbon emissions for laser-based manufacturing. A range of individual laser cutting, welding, and cleaning processes were studied to assess their energy efficiency, including the laser and its ancillary subsystems (e.g., cooling and extraction). The project developed a systematic analysis method, adapted from BS ISO 14955-1:2017, which incorporated time and subsystem level studies to quantify all energy consumption components of a laser system. Previous research has identified that the laser system's most significant contributor to the total energy consumption are the auxiliary or supporting subsystems, not the laser emission. This identified that using only the absorbed radiation to evaluate manufacturing efficiency is misleading. All the processes evaluated followed a negative correlation between processing rate (kg/h) and specific energy consumption (J/kg). The new data conclude that laser processes have a relatively high energy intensity compared to conventional manufacturing alternatives. The results can be used to identify where the most significant improvements to individual laser systems can be made. The comprehensive comparison of processes allows manufacturers to select processes to improve environmental impact.
3
Iten, Muriel, Miguel Oliveira, Diogo Costa, and Jochen Michels. "Water and Energy Efficiency Improvement of Steel Wire Manufacturing by Circuit Modelling and Optimisation." Energies 12, no. 2 (January 11, 2019): 223. http://dx.doi.org/10.3390/en12020223.
Повний текст джерелаАнотація:
Industrial water circuits (IWC) are frequently neglected as they are auxiliary circuits of industrial processes, leading to a missing awareness of their energy- and water-saving potential. Industrial sectors such as steel, chemicals, paper and food processing are notable in their water-related energy requirements. Improvement of energy efficiency in industrial processes saves resources and reduces manufacturing costs. The paper presents a cooling IWC of a steel wire processing plant in which steel billets are transformed into wire. The circuit was built in object-oriented language in OpenModelica and validated with real plant data. Several improvement measures have been identified and an optimisation methodology has been proposed. A techno-economic analysis has been carried out to estimate the energy savings and payback time for the proposed improvement measures. The suggested measures allow energy savings up to 29% in less than 3 years’ payback time and water consumption savings of approximately 7.5%.
4
Willenbacher, Martina, Jonas Scholten, and Volker Wohlgemuth. "Machine Learning for Optimization of Energy and Plastic Consumption in the Production of Thermoplastic Parts in SME." Sustainability 13, no. 12 (June 16, 2021): 6800. http://dx.doi.org/10.3390/su13126800.
Повний текст джерелаАнотація:
In manufacturing companies, especially in SMEs, the optimization of processes in terms of resource consumption, waste minimization, and pollutant emissions is becoming increasingly important. Another important driver is digitalization and the associated increase in the volume of data. These data, from a multitude of devices and systems, offer enormous potential, which increases the need for intelligent, dynamic analysis models even in smaller companies. This article presents the results of an investigation into whether and to what extent machine learning processes can contribute to optimizing energy consumption and reducing incorrectly produced plastic parts in plastic processing SMEs. For this purpose, the machine data were recorded in a plastics-producing company for the automotive industry and analyzed with regard to the material and energy flows. Machine learning methods were used to train these data in order to uncover optimization potential. Another problem that was addressed in the project was the analysis of manufacturing processes characterized by strong non-linearities and time-invariant behavior with Big Data methods and self-learning controls. Machine learning is suitable for this if sufficient training data are available. Due to the high material throughput in the production of the SMEs’ plastic parts, these requirements for the development of suitable learning methods were met. In response to the increasing importance of current information technologies in industrial production processes, the project aimed to use these technologies for sustainable digitalization in order to reduce the industry’s environmental impact and increase efficiency.
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Ingarao, Giuseppe, Paolo C. Priarone, Francesco Gagliardi, Rosa di Lorenzo, and Luca Settineri. "Environmental Comparison between a Hot Extrusion Process and Conventional Machining Processes through a Life Cycle Assessment Approach." Key Engineering Materials 622-623 (September 2014): 103–10. http://dx.doi.org/10.4028/www.scientific.net/kem.622-623.103.
Повний текст джерелаАнотація:
Nowadays manufacturing technologies have to be evaluated not only for the technical features they can provide to products, but also considering the environmental perspective as well. As long as the technological feasibility of a given process is guaranteed, processes minimizing resources and energy consumption have to be selected for manufacturing. With respect to this topic, the research studies in the domain of metal processing technologies predominantly focus on conventional material removal processes as milling and turning. Despite some exceptions, many other non-machining technologies, such as metal forming processes, are still not well documented in terms of their energy and resource efficiency, and related environmental impact. In this paper, an environmental challenge between two traditional technologies is developed: the environmental performances of a partial hot extrusion process and of a turning processes are quantified and compared. A Life Cycle Assessment (LCA) approach is implemented to properly analyze the considered processes. The material production step and the manufacturing phase to obtain a simple axy-symmetric aluminum component is considered for the Life Cycle Inventory (LCI) data collection step. Besides, the material and consumables usage and the consumed electrical power are measured in order to quantify the energy consumption of the manufacturing phase. Further, the environmental impacts related to the manufacturing of the extrusion dies and of the turning process are included in the analysis. The paper presents an early step of a wider research project aiming at identifying the greenest technologies as functions of given product features.
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Indzere, Zane, Kevin D. Manzano Martinez, Tereza Bezrucko, Zauresh Khabdullina, Ivars Veidenbergs, and Dagnija Blumberga. "Energy Efficiency Improvement in Thawing." Environmental and Climate Technologies 24, no. 2 (September 1, 2020): 221–30. http://dx.doi.org/10.2478/rtuect-2020-0068.
Повний текст джерелаАнотація:
AbstractThe thawing process within fish processing is one of the most essential steps in manufacturing. Various processes of thawing can be used where efficiency varies between companies depending on such characteristics as energy consumption, the price of resources, etc. The main aim of the research is to increase the efficiency of thawing processes. Firstly, to analyse various thawing methods and to find the most efficient one by using multi-criteria decision making analysis method. Secondly, analysing data of thawing of existing company to find opportunities for improvements, including the change of existing technology. Results showed that the most suitable method for thawing is the air blast method. Case study showed that current thawing technology is outdated, thus suggested improvement would be to replace the current boiler house with a cogeneration plant. A sensitivity analysis for the cogeneration plant has been performed.
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Brooks, Christopher, Mark Swainson, Ian Beauchamp, Isabel Campelos, Ruzaina Ishak, and Wayne Martindale. "Transformational Steam Infusion Processing for Resilient and Sustainable Food Manufacturing Businesses." Foods 10, no. 8 (July 30, 2021): 1763. http://dx.doi.org/10.3390/foods10081763.
Повний текст джерелаАнотація:
Here we show how food and beverage manufacturers report more incisive sustainability and product fulfilment outcomes for their business enterprises when innovative processing technologies are used. The reported steam infusion technology heats food materials within a Vaction Pump device so that steam is directed into the food material within a much reduced volume, reducing the use of steam and processing time. This study reports how such technological interventions will enable supply chain stakeholders to demonstrate responsible consumption by connecting assessments for the reduction of greenhouse gas emissions with consumer-focused outcomes such as product quality. The technology reported in this research not only improves operational agility by improving processing speed, but also improves the responsiveness of factory production to changes in demand. Heating procedures are systemic processes in the food industry that can be used to pasteurize, achieve commercially viable shelf-life, and provide cleaning in place. The reported research defines how these technologies can reduce the carbon footprint of products, improve quality attributes, and lower operating costs across supply chains. They provide an important step in developing distributed manufacturing in the food system because the technologies reported here are modular and can be installed into existing operations. The specific technology can reduce energy consumption by 17.3% compared to basic direct steam heating, with a reduction of 277.8 processing hours and 8.7 tonnes GHG emissions per kettle production line each year. Food and beverage manufacturers are increasingly required to report across the sustainability, nutrition, and product quality outcomes of their business enterprises more incisively so that supply chain stakeholders can demonstrate responsible production and consumption. The steam infusion technologies assessed in this research enable alignment to the UN Sustainable Development Goals, specifically SDG12, Responsible Production and Consumption, using in situ data logging in factory trials for novel heating procedures used to process foods.
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Yu, Hui Jun, Zhi Wei Zhou, Cai Biao Chen, and Ju Hui Gu. "Design of Locomotive Intelligent Watt Hour Meter Based on STM32." Applied Mechanics and Materials 672-674 (October 2014): 1205–9. http://dx.doi.org/10.4028/www.scientific.net/amm.672-674.1205.
Повний текст джерелаАнотація:
The paper focuses on the locomotive intelligent watt hour meter based on STM32. By designing Intelligent watt hour meter main function, hardware architecture, and software processes to achieve energy data display, storage, and duplex communication. The hardware structure consists of main control module, electric energy metering module, communication module and other components, electric energy metering module is responsible for data acquisition, the main control module is responsible for data processing, communications module is responsible for two-way communication. Using MATLAB software to simulate the newly developed Intelligent watt hour meter,the simulation results show that, the intelligent watt hour meter has the advantages of high precision, low power consumption, fast performance, strong anti-interference ability, low manufacturing cost etc.
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Girdu, Constantin Cristinel, and Catalin Gheorghe. "Energy Efficiency in CO2 Laser Processing of Hardox 400 Material." Materials 15, no. 13 (June 26, 2022): 4505. http://dx.doi.org/10.3390/ma15134505.
Повний текст джерелаАнотація:
The use of laser technology for materials processing has a wide applicability in various industrial fields, due to its proven advantages, such as processing time, economic efficiency and reduced impact on the natural environment. The expansion of laser technology has been possible due to the dynamics of research in the field. One of the directions of research is to establish the appropriate cutting parameters. The evolution of research in this direction can be deepened by determining the efficiency of laser cutting. Starting from such a hypothesis, the study contains an analysis of laser cutting parameters (speed, power and pressure) to determine the linear energy and cutting efficiency. For this purpose, the linear energy and the cutting efficiency were determined analytically, and the results obtained were tested with the Lagrange interpolation method, the statistical mathematical method and the graphical method. The material chosen was Hardox 400 steel with a thickness of 8 mm, due to its numerous industrial applications and the fact that it is an insufficiently studied material. Statistical data processing shows that the maximum cutting efficiency is mainly influenced by speed, followed by laser power. The results obtained reduce energy costs in manufacturing processes that use the CO2 laser. The combinations identified between laser speed and power lead to a reduction in energy consumption and thus to an increase in processing efficiency. Through the calculation relationships established for linear energy and cutting efficiency, the study contributes to the extension of the theoretical and practical basis.
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Soulis, Spyridon, George Konstantopoulos, Elias P. Koumoulos, and Costas A. Charitidis. "Impact of Alternative Stabilization Strategies for the Production of PAN-Based Carbon Fibers with High Performance." Fibers 8, no. 6 (May 26, 2020): 33. http://dx.doi.org/10.3390/fib8060033.
Повний текст джерелаАнотація:
The aim of this work is to review a possible correlation of composition, thermal processing, and recent alternative stabilization technologies to the mechanical properties. The chemical microstructure of polyacrylonitrile (PAN) is discussed in detail to understand the influence in thermomechanical properties during stabilization by observing transformation from thermoplastic to ladder polymer. In addition, relevant literature data are used to understand the comonomer composition effect on mechanical properties. Technologies of direct fiber heating by irradiation have been recently involved and hold promise to enhance performance, reduce processing time and energy consumption. Carbon fiber manufacturing can provide benefits by using higher comonomer ratios, similar to textile grade or melt-spun PAN, in order to cut costs derived from an acrylonitrile precursor, without suffering in regard to mechanical properties. Energy intensive processes of stabilization and carbonization remain a challenging field of research in order to reduce both environmental impact and cost of the wide commercialization of carbon fibers (CFs) to enable their broad application.
11
Chi, Xiongfei. "Control Algorithm of Precision Machining." E3S Web of Conferences 260 (2021): 03020. http://dx.doi.org/10.1051/e3sconf/202126003020.
Повний текст джерелаАнотація:
Due to more and more private customized non-standard design and precision manufacturing, as well as strict requirements for green environmental protection and sustainable economic development mode, it is challenging to realize the synchronous meeting of energy-saving optimization requirements in the processing process of high-precision workpiece. A new semi-automatic machining optimization system is proposed in this paper. The system is based on the high-precision 3D computer files of the workpiece to be processed and the laser thermophysical system. At the same time, the processing parameters are optimized based on the high-precision algorithm. The innovative contents of the high-precision optimization system include: (1) reciprocating fast high-frequency program design. Its single cycle task is: "core data acquisition, data in-depth analysis / machining precision refinement, key machining parameters recalibration", in order to maximize the process adaptability of the optimization system designed in this study in the highprecision machining process of different types of workpieces. (2) A new green energy-saving and environmental protection model, using primary chemical degreasing and laser derusting processing to get the most accurate 3D scanning files of parts waiting for processing, minimize the process error, so as to maximize the material and energy efficiency. In the calculation of the energy model, the most scientific consumption of coolant is considered.
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Kanoun, Olfa, Sabrine Khriji, Slim Naifar, Sonia Bradai, Ghada Bouattour, Ayda Bouhamed, Dhouha El Houssaini, and Christian Viehweger. "Prospects of Wireless Energy-Aware Sensors for Smart Factories in the Industry 4.0 Era." Electronics 10, no. 23 (November 26, 2021): 2929. http://dx.doi.org/10.3390/electronics10232929.
Повний текст джерелаАнотація:
Advanced sensors are becoming essential for modern factories, as they contribute by gathering comprehensive data about machines, processes, and human-machine interaction. They play an important role in improving manufacturing performance, in-factory logistics, predictive maintenance, supply chains, and digitalization in general. Wireless sensors and wireless sensor networks (WSNs) provide, in this context, significant advantages as they are flexible and easily deployable. They have reduced installation and maintenance costs and contributed by reducing cables and preinstalled infrastructure, leading to improved reliability. WSNs can be retrofitted in machines to provide direct information from inside the processes. Recent developments have revealed exciting possibilities to enhance energy harvesting (EH) and wireless energy transmission, enabling a reliable use of wireless sensors in smart factories. This review provides an overview of the potential of energy aware WSNs for industrial applications and shows relevant techniques for realizing a sustainable energy supply based on energy harvesting and energy transfer. The focus is on high-performance converter solutions and improvement of frequency, bandwidth, hybridization of the converters, and the newest trends towards flexible converters. We report on possibilities to reduce the energy consumption in wireless communication on the node level and on the network level, enabling boosting network efficiency and operability. Based on the existing technologies, energy aware WSNs can nowadays be realized for many applications in smart factories. It can be expected that they will play a great role in the future as an enabler for digitalization in this decisive economic sector.
13
Zhulai, Yu O., and D. D. Zahovailova. "Energy efficient technologies for the mining industry." Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, no. 6 (December 25, 2022): 11–17. http://dx.doi.org/10.33271/nvngu/2022-6/011.
Повний текст джерелаАнотація:
The cavitation generator of fluid pressure oscillations is a promising device for productivity and efficiency improvement in the mining industry (hereinafter referred to as the generator). Due to the periodic growth, separation and collapse of cavitation cavities into generator volume, shock pressure oscillations are realized with a frequency range from 1 to 20 kHz. Oscillatory pressure peak values are up by 4 times higher than the steady-state pressure at the generator inlet. The destroyed rock takes on a fatigue character under repeated alternating effects of force impulses. Due to the development of a network of microcracks in the rock, the discontinuity of the rock mass occurs at stresses lower than the rock ultimate strength. This leads to an increase in the rate of penetration, high-quality disintegration of well productive zones and an increase in their production rate, as well as to effective loosening and degassing of outburst-prone coal seams. Purpose. To conduct a systematic analysis of the use of a cavitation generator in the mining industry and evaluate its effectiveness. To develop a simplified method for calculating the maximum values of the range of fluid pressure oscillations by the generator. Methodology. The techniques are based on the study of recent research and publications on the use of the generator as a means of impulse action, and on the processing of on its dynamic parameters experimental data. Findings. The results are given in the form of the main parameters that determine the efficiency of technological processes with hydro pulse exposure. The calculation dependences of values are presented of the cavitation parameter for which of the maximum levels of the fluid oscillation are implemented on the injection pressure and those of the maximum values of the range of fluctuations on the cavitation parameter. Originality. It has been established that the use of the generator as a means of impulse action intensifies the mining industrys technological processes and leads to a significant reduction in specific energy consumption. A new simplified method for calculating the maximum level of the oscillation range has been developed, which makes it possible to determine the rational operation modes of the generator. Practical value. At the stage of designing new equipment or upgrading existing equipment, this simplified method allows determining the effective mode of operation of the generator by engineering methods to reduce the specific energy consumption of the technological process.
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Singha Roy, Pritha, Gaurab Joarder, Saibal Debnath, and Avisek Pahari. "A REVIEW OF THE INNOVATIVE DRYING TECHNOLOGIES FOR BIOPHARMACEUTICALS." International Journal of Advanced Research 10, no. 05 (May 31, 2022): 1100–1111. http://dx.doi.org/10.21474/ijar01/14820.
Повний текст джерелаАнотація:
Reviewing data from the previous twenty to twenty-five years reveals that bio-pharmaceuticals are a sudden, dramatic, and incredibly significant finding in progressively enhancing the quality of life for patients with different kinds of malignancies, auto-immune illnesses, genetic disorders, etc. Drying technologies are a required manufacturing step in the pharmaceutical industry/production unit, and an understanding of drying technologies and how to use them is now an absolute must. With the increased demand for biopharmaceuticals, it is essential to reduce production costs without sacrificing product safety, quality, or effectiveness. The predominant commercial method for generating solid biopharmaceuticals is batch freeze-drying. However, freeze-drying is expensive compared to other procedures and is not ideal for lengthy working hours, in addition to requiring a large initial capital investment and significant energy consumption, resulting in high total expenses. This article discusses innovative drying methods for parenteral biopharmaceuticals. Spin-freeze drying, spray drying, and Lynfinity® Technology enable continuous manufacturing, while PRINT® Technology and MicroglassificationTM manage dry particle characteristics. As a consequence, certain drying processes may need less validation. Process Analytical Technology (PAT) and offline dramatisation may give extra information on CPPs and CQAs during biopharmaceutical manufacture. These processing approaches might boost biopharmaceutical product production while reducing expenses.
15
Zhang, Huiling, Ruping Liu, Huiqing Zhao, Zhicheng Sun, Zilong Liu, Liang He, and Ye Li. "Research Progress of Biomimetic Memristor Flexible Synapse." Coatings 12, no. 1 (December 25, 2021): 21. http://dx.doi.org/10.3390/coatings12010021.
Повний текст джерелаАнотація:
With the development of the Internet of things, artificial intelligence, and wearable devices, massive amounts of data are generated and need to be processed. High standards are required to store and analyze this information. In the face of the explosive growth of information, the memory used in data storage and processing faces great challenges. Among many types of memories, memristors have received extensive attentions due to their low energy consumption, strong tolerance, simple structure, and strong miniaturization. However, they still face many problems, especially in the application of artificial bionic synapses, which call for higher requirements in the mechanical properties of the device. The progress of integrated circuit and micro-processing manufacturing technology has greatly promoted development of the flexible memristor. The use of a flexible memristor to simulate nerve synapses will provide new methods for neural network computing and bionic sensing systems. In this paper, the materials and structure of the flexible memristor are summarized and discussed, and the latest configuration and new materials are described. In addition, this paper will focus on its application in artificial bionic synapses and discuss the challenges and development direction of flexible memristors from this perspective.
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Alam, Md Shafayet, Imran Hossain, Gaurab Dutta, and Erica Murray. "Effects of Different Scan Speeds on Microstructural and Corrosion Properties of Additively Manufactured HSLA Steels in 3.5% NaCl Solution." ECS Meeting Abstracts MA2022-02, no. 10 (October 9, 2022): 696. http://dx.doi.org/10.1149/ma2022-0210696mtgabs.
Повний текст джерелаАнотація:
In automotive applications, high-strength low alloy (HSLA) steels are providing enhanced material properties, due to the high yield strength, less fragility, and lower weight, which also promotes lower fuel consumption. HSLA steels contain a small amount of carbon (under 0.2%) and also contain small amounts of alloying elements such as copper, nickel, niobium, vanadium, chromium, molybdenum and zirconium. This eliminates the toughness reducing effect of a pearlitic volume fraction, yet maintains and increases the material's strength by refining the grain size. Therefore, HSLA steels are used for structures intended to handle large amounts of stress or that need a good strength-to-weight ratio. Traditional manufacturing methods for steel (e.g., blast furnace or electric arc furnace methods) fail to provide the necessary optimization for best output in terms of performance and application. The quality of molten steel is greatly affected by scrap steel. The smelting period is longer, and the power consumption is large. In addition, these processes allow more impurities into the molten steel, which compromises the quality of the final product. However, additive manufacturing (AM) can can be used to fabricate HSLA steel components without these drawbacks. In addition, AM can provide a relatively faster process with low manufacturing costs, in comparison with traditional processing methods. Although AM has several benefits, studies shows that AM processes can result in HSLA steels with microstructural defects, such as non-homogeneity, internal cavities, inclusions, and impurities. Consequently, these microstructural features have a significant affect on the corrosion properties of AM parts as corrosion tends to initiate in defective regions. The AM processing parameters directly impact the microstructure of the fabricated part. Hence, it is important to understand the relationship between the AM processing parameters on resulting microstructural features. The present work evaluated the electrochemical corrosion properties of HSLA steels fabricated by AM via selective laser melting (SLM) under different processing conditions. The goal of the work was to investigate the role of microstructure on electrochemical corrosion in high-strength low alloy steels due to SLM processing conditions. Two types of HSLA steels, Fe 367 and Fe 398, were fabricated by AM via (SLM). Fe 398 differs from Fe 367 as it contains molybdenum and nickel. Each type was fabricated at a laser power of 100W, and scan speed of 600, 800, 100 and 1200 mm/s. The samples were exposed to 3.5% NaCl for 15 days, individually. To acquire the electrochemical corrosion data and to perform analysis, a Gamry reference 600 potentiostat/galvanostat was used. The electrochemical data were obtained by collecting the impedance spectra, and measuring the polarization resistance every 5 days. On day 15, cyclic polarization data was collected for each sample. These measurements helped to identify the localized corrosion as well as provide detailed information about the corrosion properties, such as passive layer growth, initiation and secession of pitting, and corrosion rate. The topography of the materials was observed by SEM before and after the corrosion tests. Energy Dispersive Spectroscopy (EDS) was performed on the samples to identify the chemical elements. The surface roughness was observed through confocal microscope. The Confocal and SEM images showed the change in surface microstructure and topographical properties of the samples before and after the corrosion testing. The difference in laser power and scan speed affected the microstructure and corrosion properties of the materials. As the samples were manufactured at same laser power, the scan speed was responsible for different topographical and corrosion behavior. Samples manufactured at lower scan speed showed less flaws on the surface of the materials than the samples manufactured at higher scan speed. Therefore, both Fe 398 and Fe 367 showed better surface topography at 600 mm/s and 800 mm/s. They also demonstrated significantly lower corrosion rate than the other samples. EDS identified chemical oxides and chlorides which were formed during the corrosion test. Overall, this work demonstrated that Fe 398 shows better microstructural and corrosion properties than Fe 367 at lower scan speed.
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Spencer, D. B., J. W. Temple, D. M. Forsythe, and B. E. Bond. "Large-Scale Rotary Shear Shredder Performance Testing." Journal of Energy Resources Technology 107, no. 2 (June 1, 1985): 289–96. http://dx.doi.org/10.1115/1.3231192.
Повний текст джерелаАнотація:
Slow-speed rotary shear shredders have recently received considerable attention for processing of municipal solid waste. Potential benefits from shear shredding could include reduced explosion potential, lower power consumption, lower operating and maintenance costs and less overgrinding of glass. Although there has been much interest in rotary shear shredders, little actual operating data exists showing the capacity and performance of these units on municipal solid waste at full scale. A large-scale, 50 tph (45.3 tonnes/hr) Iowa Manufacturing Company (Cedarapids) Model 5096 (127 cm × 244 cm) shear shredder was installed and has been evaluated over a 6-mo period at the Charleston County Solid Waste Reduction Center. Two Heil 42F vertical shaft hammermills also are operated at the reduction center. Long-term landfill tests were conducted on waste processed by both shredders to measure the performance of the shear shredder and compare the effect of shredder type on landfilling characteristics. These results show capacity in excess of 60 tph (54.4 tonnes/hr) using 4-in. (10-cm) cutters and comparable landfilling characteristics for both types of shredders.
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Aichberger, Christian, and Gerfried Jungmeier. "Environmental Life Cycle Impacts of Automotive Batteries Based on a Literature Review." Energies 13, no. 23 (December 1, 2020): 6345. http://dx.doi.org/10.3390/en13236345.
Повний текст джерелаАнотація:
We compiled 50 publications from the years 2005–2020 about life cycle assessment (LCA) of Li-ion batteries to assess the environmental effects of production, use, and end of life for application in electric vehicles. Investigated LCAs showed for the production of a battery pack per kWh battery capacity a median of 280 kWh/kWh_bc (25%-quantile–75%-quantile: 200–500 kWh/kWh_bc) for the primary energy consumption and a median of 120 kg CO2-eq/kWh_bc (25%-quantile–75%-quantile: 70–175 kg CO2-eq/kWh_bc) for greenhouse gas emissions. We expect results for current batteries to be in the lower range. Over the lifetime of an electric vehicle, these emissions relate to 20 g CO2-eq/km (25%-quantile–75%-quantile: 10–50 g CO2-eq/km). Considering recycling processes, greenhouse gas savings outweigh the negative environmental impacts of recycling and can reduce the life cycle greenhouse gas emissions by a median value of 20 kg CO2-eq/kWh_bc (25%-quantile–75%-quantile: 5–29 kg CO2-eq/kWh_bc). Overall, many LCA results overestimated the environmental impact of cell manufacturing, due to the assessments of relatively small or underutilized production facilities. Material emissions, like from mining and especially processing from metals and the cathode paste, could have been underestimated, due to process-based assumptions and non-regionalized primary data. Second-life applications were often not considered.
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Bunke, Samantha, Xi Chen, Michael Machala, Ines Azevedo, Sally Benson, and William Abraham Tarpeh. "Life Cycle Comparison of Battery Recycling and Conventional Material Refining." ECS Meeting Abstracts MA2022-01, no. 5 (July 7, 2022): 586. http://dx.doi.org/10.1149/ma2022-015586mtgabs.
Повний текст джерелаАнотація:
Background: The rise of renewable energy generation and vehicle electrification has created exponential growth in lithium-ion battery (LIB) production, particularly for electric vehicles.1 However, the limited supply of raw materials needed for prominent battery chemistries has exacerbated concerns linked to economic, environmental, national security, and human rights dimensions.2 For example, raw materials necessary for LIB production are not equally distributed, and current supply chains are insufficient for projected demand. For countries with natural reserves of critical LIB elements, the mining of ore for battery production often involves the destruction of natural ecosystems and sometimes employs child labor under harsh working conditions.3 Further, many small LIBs in consumer electronics are carelessly disposed of in garbage or recycling bins at end-of-life. Because these disposal streams are not designed to process energized batteries, LIBs have caused fires and millions of dollars in damage to waste management and recycling centers.4 Taken together, these concerns underscore the need for robust recycling programs for LIBs and their components. Methods: We performed a comparative environmental assessment of the gate-to-gate refinement process of battery material production based on conventional battery material refining versus LIB recycling by Redwood Materials. Two- and three-step recycling processes were assessed for battery feedstocks that included production scrap and mixed spent lithium-ion batteries. Data detailing energy, water, and consumables usage were provided by Redwood and normalized to elemental mass flows and products of interest. The system boundary does not include other operations at Redwood Materials outside of the direct refinement processes nor the embodied resources in the capital equipment used for material refinement. The functional unit employed in the analysis was a 1 kg of active nickel, cobalt, aluminum oxide (NCA) battery material. The primary LCA criteria evaluated included global warming potential (CO2eq), primary energy demand, and water consumption. Data for conventional material refining were adapted from the Argonne National Laboratory’s Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET®) 2020 model. GREET was also employed to acquire the life cycle parameters for producing consumables used in both conventional metal refining and Redwood Materials’ recycling processes to provide common scaling factors. Transportation between stages was not included in this analysis because it was not consistently available in the GREET model. The environmental metrics for each metal pathway were normalized by the mass of the individual element of interest within the final product and then normalized again by the mass of that element in the functional unit. Results and Implications: The goal of this study was to compare environmental metrics of two scenarios: (1) conventional refining of raw battery materials and (2) recycling of batteries by Redwood Materials. Relative to conventional refining, the Redwood Materials recycling processes reduced both water (by 70–80%) and energy consumption (by 81–87%) per kg of NCA battery active material. The Redwood processes lowered CO2 emissions by 67–68% compared to conventional ore refining. The output products of each recycling process were dependent on the battery feedstock and recycling efficiency. In the case of Ni-rich feedstock, the CO2 emissions by mass of Ni were 30–45 times lower for recycling versus conventional refining when considering only nickel as the output product. In addition to comparing recycling processes to conventional refining, we also conducted a prospective life-cycle assessment to identify optimization opportunities at Redwood. Among the three process steps (mechanical processing, low-temperature calcination, and hydrometallurgy), the hydrometallurgical process was the most resource- and carbon-intensive, counter to popular opinion. This was due to the embodied resources required to produce the input consumables Ca(OH)2, H2O2, and electricity from the Nevada electrical grid. The addition of a low-temperature calcination step prior to hydrometallurgy slightly reduced overall resource consumptions and emissions because fewer input consumables were needed for the hydrometallurgical step. Overall, this study identified value propositions and optimization opportunities for battery recycling as it reaches a much-needed scale to support the burgeoning LIB market. This assessment will guide battery recyclers on environmental targets, and inform several stakeholders (e.g., public, policymakers, electrochemists and electrochemical engineers) regarding the tradeoffs and opportunities for a circular battery economy relative to conventional battery manufacturing. References Electric Vehicles are starting to buoy the global metals market. https://www.bloombergquint.com/technology/the-relentless-march-toward-an-ev-future-is-good-news-for-miners. National Blueprint for Lithium Batteries; U. S. Department of Energy: 2021. Findings on the Worst Forms of Child Labor: Democratic Republic of the Congo. https://www.dol.gov/agencies/ilab/resources/reports/child-labor/congo-democratic-republic-drc. Staub, C. MRF operator: Lithium-ion batteries are ‘ticking time bombs’. https://resource-recycling.com/recycling/2021/04/13/mrf-operator-lithium-ion-batteries-are-ticking-time-bomb.
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Tubiello, Francesco N., Kevin Karl, Alessandro Flammini, Johannes Gütschow, Griffiths Obli-Laryea, Giulia Conchedda, Xueyao Pan, et al. "Pre- and post-production processes increasingly dominate greenhouse gas emissions from agri-food systems." Earth System Science Data 14, no. 4 (April 14, 2022): 1795–809. http://dx.doi.org/10.5194/essd-14-1795-2022.
Повний текст джерелаАнотація:
Abstract. We present results from the FAOSTAT emissions shares database, covering emissions from agri-food systems and their shares to total anthropogenic emissions for 196 countries and 40 territories for the period 1990–2019. We find that in 2019, global agri-food system emissions were 16.5 (95 %; CI range: 11–22) billion metric tonnes (Gt CO2 eq. yr−1), corresponding to 31 % (range: 19 %–43 %) of total anthropogenic emissions. Of the agri-food system total, global emissions within the farm gate – from crop and livestock production processes including on-farm energy use – were 7.2 Gt CO2 eq. yr−1; emissions from land use change, due to deforestation and peatland degradation, were 3.5 Gt CO2 eq. yr−1; and emissions from pre- and post-production processes – manufacturing of fertilizers, food processing, packaging, transport, retail, household consumption and food waste disposal – were 5.8 Gt CO2 eq. yr−1. Over the study period 1990–2019, agri-food system emissions increased in total by 17 %, largely driven by a doubling of emissions from pre- and post-production processes. Conversely, the FAOSTAT data show that since 1990 land use emissions decreased by 25 %, while emissions within the farm gate increased 9 %. In 2019, in terms of individual greenhouse gases (GHGs), pre- and post-production processes emitted the most CO2 (3.9 Gt CO2 yr−1), preceding land use change (3.3 Gt CO2 yr−1) and farm gate (1.2 Gt CO2 yr−1) emissions. Conversely, farm gate activities were by far the major emitter of methane (140 Mt CH4 yr−1) and of nitrous oxide (7.8 Mt N2O yr−1). Pre- and post-production processes were also significant emitters of methane (49 Mt CH4 yr−1), mostly generated from the decay of solid food waste in landfills and open dumps. One key trend over the 30-year period since 1990 highlighted by our analysis is the increasingly important role of food-related emissions generated outside of agricultural land, in pre- and post-production processes along the agri-food system, at global, regional and national scales. In fact, our data show that by 2019, pre- and post-production processes had overtaken farm gate processes to become the largest GHG component of agri-food system emissions in Annex I parties (2.2 Gt CO2 eq. yr−1). They also more than doubled in non-Annex I parties (to 3.5 Gt CO2 eq. yr−1), becoming larger than emissions from land use change. By 2019 food supply chains had become the largest agri-food system component in China (1100 Mt CO2 eq. yr−1), the USA (700 Mt CO2 eq. yr−1) and the EU-27 (600 Mt CO2 eq. yr−1). This has important repercussions for food-relevant national mitigation strategies, considering that until recently these have focused mainly on reductions of non-CO2 gases within the farm gate and on CO2 mitigation from land use change. The information used in this work is available as open data with DOI https://doi.org/10.5281/zenodo.5615082 (Tubiello et al., 2021d). It is also available to users via the FAOSTAT database (https://www.fao.org/faostat/en/#data/EM; FAO, 2021a), with annual updates.
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Li, Bing Bing, Hong Chao Zhang, Qing Di Ke, Li Ding, and Lei Zhang. "Overview of Energy Consumption Model for Manufacturing Processes." Applied Mechanics and Materials 130-134 (October 2011): 2288–93. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2288.
Повний текст джерелаАнотація:
The energy consumption for manufacturing processes is the largest impact contributor in various characterization categories, based on the assessment of environmental effects during the whole life cycle. It is necessary to investigate the manufacturing processes in depth to find out mechanism that can improve energy efficiency. This paper presents a comprehensive overview on two important aspects of energy consumption models for manufacturing processes: 1) two data collection methods: top-down and bottom-up; 2) two process-based analytical methods: thermodynamic model (including energy flow analysis and exergy analysis), and mechanical model. These models can improve energy efficiency.
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Xiao, Yongmao, Qingshan Gong, and Xiaowu Chen. "Energy Saving and Low-Cost-Oriented Design Processes of Blank’s Dimensions Based on Multi-Objective Optimization Model." Processes 7, no. 11 (November 4, 2019): 811. http://dx.doi.org/10.3390/pr7110811.
Повний текст джерелаАнотація:
The blank’s dimensions are an important focus of blank design as they largely determine the energy consumption and cost of manufacturing and further processing the blank. To achieve energy saving and low cost during the optimization of blank dimensions design, we established energy consumption and cost objectives in the manufacturing and further processing of blanks by optimizing the parameters. As objectives, we selected the blank’s production and further processing parameters as optimization variables to minimize energy consumption and cost, then set up a multi-objective optimization model. The optimal blank dimension was back calculated using the parameters of the minimum processing energy consumption and minimum cost state, and the model was optimized using the non-dominated genetic algorithm-II (NSGA-II). The effect of designing blank dimension in saving energy and costs is obvious compared with the existing methods.
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I.J Agabi and J.S Ibrahim. "Energy Evaluation and Processing Cost Reduction in Agudu Maize Processing Industry." International Journal of Engineering and Management Research 11, no. 1 (February 27, 2021): 142–55. http://dx.doi.org/10.31033/ijemr.11.1.20.
Повний текст джерелаАнотація:
This study evaluated energy consumption by Agudu Farms Limited (AFL) that processes maize and cassava into flour for human consumption. The objectives of study included to determine energy contribution to processing cost, to minimize the processing cost and to propose a new selling price per unit of sale of the product. The study materials included; a multi-meter, stopwatch, electrical appliances’ nameplates and bills, fuel purchased receipts, and production records. Data was collected through detailed energy audits and measurements of present electricity consumption. This data was converted into energy intensities, rates and costs, and analyzed. The monthly energy intensity plotted on bar charts using Microsoft excel and the results showed that diesel had the highest consumption variation of 3500 kWh/t, electricity 200kWh/t and labor 110 kWh/t. The percentage of energy contribution to processing cost was 33%. In monetary terms, the processing cost per hour of operation showed average value of ₦830. Whereas, the minimum production cost per hour using Tora software showed ₦767. The new product price per ten-kilogram (10kg) unit of sale of maize flour, using break-even analysis, showed ₦2864. The study observed that diesel contributed more to production cost than electricity and labor and therefore, recommended the setting up of an energy monitoring team to monitor procurement and control utilization of diesel to reduce production cost.
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Pătrașcu, Roxana, Eduard Minciuc, George Darie, Ștefan-Dominic Voronca, and Andreea-Ioana Bădicu. "Energy efficiency solutions for driers used in the glass manufacturing and processing industry." Proceedings of the International Conference on Business Excellence 11, no. 1 (July 1, 2017): 199–208. http://dx.doi.org/10.1515/picbe-2017-0021.
Повний текст джерелаАнотація:
Abstract Energy conservation is relevant to increasing efficiency in energy projects, by saving energy, by its’ rational use or by switching to other forms of energy. The goal is to secure energy supply on short and long term, while increasing efficiency. These are enforced by evaluating the companies’ energy status, by monitoring and adjusting energy consumption and organising a coherent energy management. The manufacturing process is described, starting from the state and properties of the raw material and ending with the glass drying technological processes involved. Raw materials are selected considering technological and economic criteria. Manufacturing is treated as a two-stage process, consisting of the logistic, preparation aspect of unloading, transporting, storing materials and the manufacturing process itself, by which the glass is sifted, shredded, deferrized and dried. The interest of analyzing the latter is justified by the fact that it has a big impact on the final energy consumption values, hence, in order to improve the general performance, the driers’ energy losses are to be reduced. Technological, energy and management solutions are stated to meet this problem. In the present paper, the emphasis is on the energy perspective of enhancing the overall efficiency. The case study stresses the effects of heat recovery over the efficiency of a glass drier. Audits are conducted, both before and after its’ implementation, to punctually observe the balance between the entering and exiting heat in the drying process. The reduction in fuel consumption and the increase in thermal performance and fuel usage performances reveal the importance of using all available exiting heat from processes. Technical faults, either in exploitation or in management, lead to additional expenses. Improving them is in congruence with the energy conservation concept and is in accordance with the Energy Efficiency Improvement Program for industrial facilities.
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Guo, Jiangtao, Yajie Li, Mao Fan, and Wanzhen Ma. "Dynamic Monitoring Method of Enterprise Power Consumption Based on Energy Big Data." Journal of Physics: Conference Series 2254, no. 1 (April 1, 2022): 012022. http://dx.doi.org/10.1088/1742-6596/2254/1/012022.
Повний текст джерелаАнотація:
Abstract With the global shortage of resources and energy and the intensification of environmental pollution, the problems of energy consumption and pollution emission in the manufacturing industry have become increasingly prominent. Green development, quality improvement and efficiency increase have gradually become an important development trend of the manufacturing industry. Green manufacturing engineering must be vigorously constructed and developed. Discrete manufacturing is characterized by discontinuous processes. The manufacturing process is accompanied by a large amount of primary energy consumption and environmental emissions. Compared with process manufacturing, discrete manufacturing process control is more complex and changeable, its green development level needs to be improved, and the implementation process is more difficult. As one of the common workshops in discrete manufacturing, NC workshop has problems such as high energy consumption. Based on the above reasons, this paper takes the energy-saving optimization and energy management of discrete manufacturing enterprises as the research goal, and carries out the research on energy-saving optimization and energy management system for NC workshop. It is of great significance to reduce the energy consumption of NC workshop, improve the energy utilization efficiency, solve the pain points of many enterprise information islands and difficult to control energy consumption, and improve the green level of production process in discrete manufacturing enterprises.
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Shulaev, Nikolay S., Tatiana V. Shulaeva, and Sergey V. Laponov. "Energy consumption of emulsification processes in small-sized mix apparatuses." Butlerov Communications 61, no. 1 (January 31, 2020): 86–90. http://dx.doi.org/10.37952/roi-jbc-01/20-61-1-86.
Повний текст джерелаАнотація:
There is given in this thesis a general method of calculation of power consumption for emulgation in systems liquid-liquid in small-size mixing devices (rotor-pulsation apparatuses and rotor-disc mixers). This mixing devices, have shown a high efficiency at processing of liquid-liquid systems and are wide using chemical processes. The base of calculation method is an energy ratio to describe of developed turbulent motion, pulsation intensity of which is enough for create of dispersed particles of given size and concentration, which provide a necessary surface of phases contact. It is shown, that in determination of energy consumption it is need to take in account energy dissipation processes, due to viscous friction forces, which have a significant influence at high gradients of turbulent motion. There is obtained a ratio, which connects an angular velocity of rotor rotation of mixing device and a characterized size of dispersed phase particles.There is given an experimental dependencies of consumed power of rotor-disc mixers on rotor rotations number of mixing device and characterized sizes of dispersed particles for systems water-diesel fuel. It was determined, that are decreasing of dispersed particles sizes and in increasing of volumetric flow of processing mixture a value of consumed power increases, and it is related with by increase of energy consumption for creating of interphase surface. It was determined, that a power, consumed by rotor-disc mixer, for emulsion making with averaged dispersion size of particles at range 5-25 mkm, increases by increase of rotation numbers ~n0.37. Comparison of theoretical equations and experimental data have shown adequacy of supposed calculation method of energy consumption.
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Kellens, Karel, Renaldi Renaldi, Wim Dewulf, Jean-pierre Kruth, and Joost R. Duflou. "Environmental impact modeling of selective laser sintering processes." Rapid Prototyping Journal 20, no. 6 (October 20, 2014): 459–70. http://dx.doi.org/10.1108/rpj-02-2013-0018.
Повний текст джерелаАнотація:
Purpose – This paper aims to present parametric models to estimate the environmental footprint of the selective laser sintering (SLS)’ production phase, covering energy and resource consumption as well as process emissions. Additive manufacturing processes such as (SLS) are often considered to be more sustainable then conventional manufacturing methods. However, quantitative analyses of the environmental impact of these processes are still limited and mainly focus on energy consumption. Design/methodology/approach – The required Life Cycle Inventory data are collected using the CO2PE! – Methodology, including time, power, consumables and emission studies. Multiple linear regression analyses have been applied to investigate the interrelationships between product design features on the one hand and production time (energy and resource consumption) on the other hand. Findings – The proposed parametric process models provide accurate estimations of the environmental footprint of SLS processes based on two design features, build height and volume, and help to identify and quantify measures for significant impact reduction of both involved products and the supporting machine tools. Practical implications – The gained environmental insight can be used as input for ecodesign activities, as well as environmental comparison of alternative manufacturing process plans. Originality/value – This article aims to overcome the current lack of environmental impact models, covering energy and resource consumption as well as process emissions for SLS processes.
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Raharno, Sri, Yatna Yuwana Martawirya, and Jeffry Aditya Cipta Wijaya. "Development of a Methodology for Assessing the Energy Efficiency Performance on Turning Processes." Applied Mechanics and Materials 842 (June 2016): 19–23. http://dx.doi.org/10.4028/www.scientific.net/amm.842.19.
Повний текст джерелаАнотація:
This paper aimed to develop a methodology for assessing environmental friendliness of turning processes from energy consumption’s perspective. This methodology was limited on the process level study, which turning process was chosen as the assessed process. Recently, the green manufacturing has become a very important matter due to environmental impacts caused by manufacturing processes. Reducing the amounts of input energy or increasing efficiencies of production equipment’s can help to achieve the green manufacturing level, but it does not indicate the level of energy consumption. Therefore, a methodology is needed to determine how green a manufacturing process on energy consumption’s perspective. In this case, an energy indicator can be used to evaluate the energy usage performance. Based on the experimental data from several different machines, regression lines is constructed (using data envelopment analysis) as the efficiency reference values. According to the position of the energy efficiency from the assessed process to the efficiency reference values, it will determine whether the process has a high or low efficiency (as the assessment result). This methodology has tried to indicate the level of energy consumption, by comparing the result from energy indicator with the reference value. Energy indicator for material removing processes is commonly used to predict the total energy consumption for energy assessment without comparing the result to the reference value.
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Gao, Shang, Chang Liu, and Guan Wang. "Multi-objective Green Flexible Workshop Scheduling Considering Multi-energy Consumption Factors." Journal of Physics: Conference Series 2174, no. 1 (January 1, 2022): 012083. http://dx.doi.org/10.1088/1742-6596/2174/1/012083.
Повний текст джерелаАнотація:
Abstract In order to solve the energy consumption problem of shop floor caused by machine processing, adjustment, idling and workpiece transportation, the green flexible workshop scheduling problem was studied, and a multi-objective optimization model was established with the maximum completion time, total machine load and total energy consumption as the objectives. The double-layer coding strategy was adopted to encode processes and machines respectively to optimize the scheduling scheme, and then the adaptive hybrid crossover scheme, mutation scheme and improved elite retention strategy were adopted to improve the operation efficiency and population diversity of NSGA-II algorithm. Finally, a case study is used to verify the effectiveness of the algorithm in solving the multi-objective green flexible workshop scheduling problem, which provides a reference for enterprises to achieve green manufacturing.
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Baranova-Shishkova, Lilia I., Karina S. Simpolskaya, Elena Zvonareva, and Vladimir V. Goncharenko. "Research Methods of Making Glass and its Physico-Chemical Properties." Materials Science Forum 968 (August 2019): 168–75. http://dx.doi.org/10.4028/www.scientific.net/msf.968.168.
Повний текст джерелаАнотація:
This article discusses the modern methods of physico-chemical properties of glass manufacturing, through the use of a wide spectrum of glass-forming. Description of the implementation of the staged methods of chemical-technological processes of silicate formation, implying the lowest energy consumption, in connection with the transition to the processing of secondary raw materials.
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Sheikhgasanov, Shamsutdin Kadievich, and Yury Vasil'evich Kolotilov. "Methods for achieving energy efficiency in cloud computing." Vestnik of Astrakhan State Technical University. Series: Management, computer science and informatics 2020, no. 2 (April 30, 2020): 77–83. http://dx.doi.org/10.24143/2072-9502-2020-2-77-83.
Повний текст джерелаАнотація:
The article considers the problem of cloud computing - the environment of data storage and processing that provides access to resources shared among multiple users. Cloud computing is a model for organizing remote access on request for a shared set of configurable computing resources that can be quickly allocated and freed with minimal management or interaction costs with the service provider. Using clouds significantly reduces the costs of large industrial companies and enterprises. Cloud technology helps scale your business quickly and with minimal cost, they can improve productivity together with simplifying many business processes. Cloud computing is vastly expanding opportunities, so that all large companies today are actively switching to cloud services. But this causes significant problems with energy consumption. The energy consumption by cloud computing remains a serious problem, as data processing centers grow in size. There has been proposed the approach to choosing an energy-efficient cloud architecture that aims to reduce the energy consumption of cloud applications in all deployment models, the architectures of cloud infrastructures being given. The architecture supports energy efficiency in building, deploying and operating services.
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Nota, Giancarlo, Francesco David Nota, Domenico Peluso, and Alonso Toro Lazo. "Energy Efficiency in Industry 4.0: The Case of Batch Production Processes." Sustainability 12, no. 16 (August 17, 2020): 6631. http://dx.doi.org/10.3390/su12166631.
Повний текст джерелаАнотація:
We derived a promising approach to reducing the energy consumption necessary in manufacturing processes from the combination of management methodologies and Industry 4.0 technologies. Based on a literature review and experts’ opinions, this work contributes to the efficient use of energy in batch production processes combining the analysis of the overall equipment effectiveness with the study of variables managed by cyber-physical production systems. Starting from the analysis of loss cause identification, we propose a method that obtains quantitative data about energy losses during the execution of batch processes. The contributions of this research include the acquisition of precise information about energy losses and the improvement of value co-creation practices so that energy consumption can be reduced in manufacturing processes. Decision-makers can use the findings to start a virtuous process aiming at carbon footprint and energy costs reductions while ensuring production goals are met.
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Pratama, I. Putu Agastya Harta, I. Made Sukarsa, and Gusti Agung Ayu Putri. "Reengineering of Manufacturing Business Process Utilising the Manufacturing Module of an ERP Application." Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) 9, no. 3 (October 4, 2021): 263. http://dx.doi.org/10.24843/jim.2021.v09.i03.p07.
Повний текст джерелаАнотація:
The significant change in the consumption pattern of Indonesia’s modernising society––further intensified by the Indonesian Government’s plan to push towards the era of the Industrial Revolution 4.0, has fostered the need for manufacturing companies to create newly automated processes and make the transition to become a digitalised business entity. Enterprise Resource Planning is a software that enables the creation of integration and automation of business processes between various departments within a company, thus creating an effective business strategy. The data that has been acquired from research at a poultry processing company are then being mapped into the ERP software of Odoo V12.0, therefore creating new sets of re-engineered business processes. There are seven newly proposed reengineering business processes––in which only concentrate on the production and technical department within the particular company, by utilising the manufacturing and maintenance modules. The re-engineered business processes are assessed using the Technology Acceptance Model theory, to measure its usability and practicality, which generates the score of Likert’s interpretation above the accepted standard of 1340 and 1227, for both the manufacturing and maintenance modules respectively.
Keywords: Business Process Reengineering, Enterprise Resource Planning, Manufacturing Process, Odoo, Technology Acceptance Model.
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Elahi, Mahboob, Samuel Olaiya Afolaranmi, Wael M. Mohammed, and Jose Luis Martinez Lastra. "Energy-Based Prognostics for Gradual Loss of Conveyor Belt Tension in Discrete Manufacturing Systems." Energies 15, no. 13 (June 27, 2022): 4705. http://dx.doi.org/10.3390/en15134705.
Повний текст джерелаАнотація:
This paper presents a data-driven approach for the prognosis of the gradual behavioural deterioration of conveyor belts used for the transportation of pallets between processing workstations of discrete manufacturing systems. The approach relies on the knowledge of the power consumption of a conveyor belt motor driver. Data are collected for two separate cases: the static case and dynamic case. In the static case, power consumption data are collected under different loads and belt tension. These data are used by a prognostic model (artificial neural network (ANN)) to learn the conveyor belt motor driver’s power consumption pattern under different belt tensions and load conditions. The data collected during the dynamic case are used to investigate how the belt tension affects the movement of pallets between conveyor zones. During the run time, the trained prognostic model takes real-time power consumption measurements and load information from a testbench (a discrete multirobot mobile assembling line) and predicts a belt tension class. A consecutive mismatch between the predicted belt tension class and optimal belt tension class is an indication of failure, i.e., a gradual loss of belt tension. Hence, maintenance steps must be taken to avoid further catastrophic situations such as belt slippages on head pulleys, material slippages and belt wear and tear.
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Lv, Lishu, Zhaohui Deng, Can Yan, Tao Liu, Linlin Wan, and Qianwei Gu. "Modelling and analysis for processing energy consumption of mechanism and data integrated machine tool." International Journal of Production Research 58, no. 23 (May 14, 2020): 7078–93. http://dx.doi.org/10.1080/00207543.2020.1756508.
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Robinson, David Charles, David Adrian Sanders, and Ebrahim Mazharsolook. "Ambient intelligence for optimal manufacturing and energy efficiency." Assembly Automation 35, no. 3 (August 3, 2015): 234–48. http://dx.doi.org/10.1108/aa-11-2014-087.
Повний текст джерелаАнотація:
Purpose – This paper aims to describe the creation of innovative and intelligent systems to optimise energy efficiency in manufacturing. The systems monitor energy consumption using ambient intelligence (AmI) and knowledge management (KM) technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Design/methodology/approach – Energy consumption data (ECD) are processed within a service-oriented architecture-based platform. The platform provides condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase and continuous improvement/optimisation of energy efficiency. The systems monitor energy consumption using AmI and KM technologies. Together they create a decision support system as an innovative add-on to currently used energy management systems. Findings – The systems produce an improvement in energy efficiency in manufacturing small- and medium-sized enterprises (SMEs). The systems provide more comprehensive information about energy use and some knowledge-based support. Research limitations/implications – Prototype systems were trialled in a manufacturing company that produces mooring chains for the offshore oil and gas industry, an energy intensive manufacturing operation. The paper describes a case study involving energy-intensive processes that addressed different manufacturing concepts and involved the manufacture of mooring chains for offshore platforms. The system was developed to support online detection of energy efficiency problems. Practical implications – Energy efficiency can be optimised in assembly and manufacturing processes. The systems produce an improvement in energy efficiency in manufacturing SMEs. The systems provide more comprehensive information about energy use and some knowledge-based support. Social implications – This research addresses two of the most critical problems in energy management in industrial production technologies: how to efficiently and promptly acquire and provide information online for optimising energy consumption and how to effectively use such knowledge to support decision making. Originality/value – This research was inspired by the need for industry to have effective tools for energy efficiency, and that opportunities for industry to take up energy efficiency measures are mostly not carried out. The research combined AmI and KM technologies and involved new uses of sensors, including wireless intelligent sensor networks, to measure environment parameters and conditions as well as to process performance and behaviour aspects, such as material flow using smart tags in highly flexible manufacturing or temperature distribution over machines. The information obtained could be correlated with standard ECD to monitor energy efficiency and identify problems. The new approach can provide effective ways to collect more information to give a new insight into energy consumption within a manufacturing system.
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Tomažič, Simon, Goran Andonovski, Igor Škrjanc, and Vito Logar. "Data-Driven Modelling and Optimization of Energy Consumption in EAF." Metals 12, no. 5 (May 9, 2022): 816. http://dx.doi.org/10.3390/met12050816.
Повний текст джерелаАнотація:
In the steel industry, the optimization of production processes has become increasingly important in recent years. Large amounts of historical data and various machine learning methods can be used to reduce energy consumption and increase overall time efficiency. Using data from more than two thousand electric arc furnace (EAF) batches produced in SIJ Acroni steelworks, the consumption of electrical energy during melting was analysed. Information on the consumed energy in each step of the electric arc process is essential to increase the efficiency of the EAF. In the paper, four different modelling approaches for predicting electrical energy consumption during EAF operation are presented: linear regression, k-NN modelling, evolving and conventional fuzzy modelling. In the learning phase, from a set of more than ten regressors, only those that have the greatest impact on energy consumption were selected. The obtained models that can accurately predict the energy consumption are used to determine the optimal duration of the transformer profile during melting. The models can predict the optimal energy consumption by selecting pre-processed training data, where the main steps are to find and remove outlier batches with the highest energy consumption and identify the influencing variables that contribute most to the increased energy consumption. It should be emphasised that the electrical energy consumption was too high in most batches only because the melting time was unnecessarily prolonged. Using the proposed models, EAF operators can obtain information on the estimated energy consumption before batch processing depending on the scrap weight in each basket and the added additives, as well as information on the optimal melting time for a given EAF batch. All models were validated and compared using 30% of all data, with the fuzzy model in particular providing accurate prediction results. It is expected that the use of the developed models will lead to a reduction in energy consumption as well as an increase in EAF efficiency.
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Kübler, Frank, Thomas H. J. Uhlemann, Justus Dill, and Rolf Steinhilper. "Energy Efficiency and Productivity Optimization of Industrial Cleaning Equipment." Applied Mechanics and Materials 805 (November 2015): 265–72. http://dx.doi.org/10.4028/www.scientific.net/amm.805.265.
Повний текст джерелаАнотація:
Advanced cleanliness requirements in production are forcing industrial companies to include new cleaning processes into their manufacturing process. Complex cleaning operation procedures can lower process productivity and at the same time are responsible for substantial parts of the overall energy consumption. An optimization of cleaning processes with respect to cleaning duration, energy consumption and efficiency can therefore contribute to cost reduction significantly. This article presents a procedure for real data based assessment of industrial cleaning equipment. Based upon the resulting information of the procedure, productivity ratios and energy consumptions can be determined up to individual cleaning components. This creates the required transparency to derive customized production and energy efficiency optimization measures.
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Siegel, R. P. "Minimal Solutions." Mechanical Engineering 140, no. 01 (January 1, 2018): 37–41. http://dx.doi.org/10.1115/1.2018-jan-3.
Повний текст джерелаАнотація:
This article discusses aspects of different manufacturing developments where manufacturers are working with researchers to develop ways to make products using less material and energy. Manufacturers looking to make American factories more competitive with foreign-based facilities are finding opportunities through re-engineering long-held wasteful practices. As a part of a multi-pronged approach, Pradeep Rohatgi, professor of materials science and engineering at the University of Wisconsin, Milwaukee, is leading an effort at reducing embodied-energy and decreasing emissions (REMADE) to examine manufacturing processes. REMADE aims to develop technology enablers to accomplish such goals as reducing primary feedstock consumption in manufacturing by 30 percent, reducing energy demand of secondary material processing by 30 percent, and achieving a 25 percent improvement in embodied energy efficiency of materials such as metals, polymers, fibers, and electronic waste. According to an expert, with embedded energy as the measuring stick, it is also possible to find manufacturing processes that produce very low life-cycle energy costs using carbon fiber composites or high-strength steels.
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Fatima, Anis, and Amir Iqbal Syed. "Identifying Direct Electrical Energy Demand in Wire-Cut EDM." January 2020 39, no. 1 (January 1, 2020): 171–78. http://dx.doi.org/10.22581/muet1982.2001.16.
Повний текст джерелаАнотація:
Non-traditional machining processes are popular for generating complex features on the work piece. With advances in material engineering, new ways of cutting technologies has been emerged. However, EDM (Electric Discharge Machining) has gained recognition for producing extraordinary surface finished, intricate part geometries with accuracy and its ability to cut through difficult to machined materials. However, like every product cycle, manufacturing processes also require energy to convert raw materials into finished product. In manufacturing operations, energy input gives carbon footprints which have an effect on our environment. It is observed that reducing energy consumption is becoming the main concern of manufacturers because of enforcing environmental laws and due to the economics of the processing. It is argued that world’s 70% of energy consumption is consumed by manufacturing sector. The aim of the work was to identify direct energy demands in wire cut EDM. The variability in energy demand was explored by operating wire cut EDM at no-load and loaded conditions.Stainless steel S304 was used as a work piece. Experiments were performed on three different wire-cut EDM.Molybdenum wire, brass wire and copper wire were used as an electrode wire and distilled water was used as a working fluid. During the experiment, electrical current was measured and the variation of power requirement was evaluated. Power required by different features of EDM was compared with the existing energy models and factors were identified that consume most of the electrical energy. Further, a comparison is made between traditional and non-traditional machining processes. This contribution will help to assess energy efficiency of EDM technology and identify priority areas for improvements. This work is also significant for machine tool designers for optimum utilization of energy,reduced environmental impact and reduced production cost of their machine tool.
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Eryilmaz, Derya, Jeffrey Apland, and Timothy M. Smith. "Dynamic Electricity Pricing – Modeling Manufacturer Response and an Application to Cement Processing." Energy and Environment Research 9, no. 2 (July 31, 2019): 1. http://dx.doi.org/10.5539/eer.v9n2p1.
Повний текст джерелаАнотація:
Dynamic pricing, also known as real-time pricing, provides electricity users with an economic incentive to adjust electricity use based on changing market conditions. This paper studies the economic implications of real-time pricing mechanisms in a cement manufacturing plant. Production for a representative cement manufacturing plant is modeled using stochastic mathematical programming. The results show that a cement plant can a) reduce electricity costs by shifting electricity load of certain processes to times when electricity prices are lower, and b) profitably reduce electricity use during peak prices through more efficient scheduling of production under real-time pricing compared to fixed pricing. The results suggest that building scheduling flexibility into certain industrial manufacturing processes to reschedule electricity consumption when the electricity prices at their peak may be economical. The results also suggest that shifts in the production schedule of a cement manufacturer that result from real-time pricing may also influence environmental impacts. The modelling framework modeled real-time pricing as a source of risk in this study, which is also applicable to other energy intensive industries. As such, dynamic pricing strategies that include the non-market impacts of electricity generation should be further explored.
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González-Penella, Vicente J., Francisco Beltrán Berenguer, Jaime Martínez Verdú, Celia Guillem López, Ángel M. López-Buendía, and Javier Martínez-Mingote Navarro. "Energy Analysis in the Natural Stone Manufacturing Process." Key Engineering Materials 548 (April 2013): 57–64. http://dx.doi.org/10.4028/www.scientific.net/kem.548.57.
Повний текст джерелаАнотація:
The current socio-economic situation, characterized by energy problems and environmental concerns, urgently requires an innovative intervention. Such action should be carried out in order to improve competitiveness and the strength of companies in general, and those belonging to the natural stone sector in particular. Production processes provide the largest source of data within a factory and it is therefore necessary to carry out an energy analysis, as it is a prerequisite for informed decision-making. This energy analysis would identify those areas where most energy is consumed. Consequently, an energy analysis was conducted on two of the most representative production processes (slab and tile production). First of all, researchers analysed each stage of the production process using IDEF0 standard notation. Previously, the power consumption was monitored from both electrical and thermal perspective and, using several energy indicators, an energy balance had been carried out. As a result of this analysis, the significance of electricity compared to gas was clearly identified; the analysis also showed that cutting and abrasive processes were more critical from an electrical point of view (in that order). Another important finding was that related to thermal energy: reinforcement processes were those with the greatest significance (and in particular those performed in ovens). Based on the conclusions of the energy analysis, it was possible to establish new research lines: design of cutting disc and multiblade framesaws, microwave curing technology applications, etc. Those research lines would therefore provide the most significant beneficial effects on the environment and energy saving and, hence, companies can overcome the current economic situation.
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Loukopoulos, Andreas, Christos Vasilios Katsiropoulos, and Spiros G. Pantelakis. "Carbon footprint and financial evaluation of an aeronautic component production using different manufacturing processes." International Journal of Structural Integrity 10, no. 3 (June 10, 2019): 425–35. http://dx.doi.org/10.1108/ijsi-07-2018-0043.
Повний текст джерелаАнотація:
Purpose The purpose of this paper is to quantify the environmental footprint and cost and thus compare different manufacturing scenarios associated with the production of aeronautical structural components. Design/methodology/approach A representative helicopter canopy, i.e., canopy of the EUROCOPTER EC Twin Star helicopter described in Pantelakis et al. (2009), has been considered for the carbon footprint (life cycle energy and climate change impact analysis) along with the life cycle costing analysis. Four scenarios – combinations of different manufacturing technologies (autoclave and resin transfer molding (RTM)) and end-of-life treatment scenarios (mechanical recycling and pyrolysis) are considered. Findings Using the models developed the expected environmental and cost benefits by involving the RTM technique have been quantified. The environmental impact was expressed in terms of energy consumption and of Global Warming Potential-100. From an environmental standpoint, processing the canopy using the RTM technique leads to decreased energy demands as compared to autoclaving because of the shorter curing cycles exhibited from this technique and thus the less time needed. As far as the financial viability of both processing scenarios is concerned, the more steps needed for preparing the mold and the need for auxiliary materials increase the material and the labor cost of autoclaving as compared to RTM. Originality/value At the early design stages in aeronautics, a number of disciplines (environmental, financial and mechanical) should be taken into account in order to evaluate alternative scenarios (material, manufacturing, recycling, etc.). In this paper a methodology is developed toward this direction, quantifying the environmental and financial viability of different manufacturing scenarios associated with the production of aeronautical structures.
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Makarova, Irina Leonidovna, Anna Mikhailovna Ignatenko, and Andrey Sergeevich Kopyrin. "Detection and interpretation of erroneous data in statistical analysis of consumption of energy resources." Программные системы и вычислительные методы, no. 3 (March 2021): 40–51. http://dx.doi.org/10.7256/2454-0714.2021.3.36564.
Повний текст джерелаАнотація:
Monitoring and analysis of consumption of energy resources in various contexts, as well as measuring of parameters (indicators) in time are of utmost importance for the modern economy. This work is dedicated to examination and interpretation of the anomalies of collecting data on consumption of energy resources (on the example of gas consumption) in the municipal formation. Gas consumption is important for the socioeconomic sphere of cities. Unauthorized connections are the key reason for non-technological waste of the resource. The traditional methods of detection of stealing of gas are ineffective and time-consuming. The modern technologies of data analysis would allow detecting and interpreting the anomalies of consumption, as well as forming the lists for checking the objects for unauthorized connections. The author’s special contribution lies in application of the set of statistical methods aimed at processing and identification of anomalies in energy consumption of a municipal formation. It is worth noting that the use of such technologies requires the development of effective algorithms and implementation of automation and machine learning algorithms. The new perspective upon time-series data facilitates identification of anomalies, optimization of decision-making, etc. These processes can be automated. The presented methodology tested on time-series data that describes the consumption of gas can be used for a broader range of tasks. The research can be combined with the methods of knowledge discovery and deep learning algorithms.
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Shin, Seung-Jun, Young-Min Kim, and Prita Meilanitasari. "A Holonic-Based Self-Learning Mechanism for Energy-Predictive Planning in Machining Processes." Processes 7, no. 10 (October 14, 2019): 739. http://dx.doi.org/10.3390/pr7100739.
Повний текст джерелаАнотація:
The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufacturing objects under the paradigm of Holonic Manufacturing Systems. This mechanism allows machines and manufacturing objects to acquire their data and model interconnection and to perform model-driven autonomous and collaborative behaviors. The hybrid learning approach is designed to obtain predictive modeling ability in both data-existent and even data-absent environments via accommodating machine learning (which extracts knowledge from data) and transfer learning (which extracts knowledge from existing knowledge). The present work also implements a prototype system to demonstrate automatic predictive modeling and autonomous process planning for energy reduction in milling processes. The prototype generates energy-predictive models via hybrid learning and seeks the minimum energy-using machine tool through the contract net protocol combined with energy prediction. As a result, the prototype could achieve a reduction of 9.70% with respect to energy consumption as compared with the maximum energy-using machine tool.
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Aboud, Salam A., Ammar B. Altemimi, Asaad R. S. Al-HiIphy, Lee Yi-Chen, and Francesco Cacciola. "A Comprehensive Review on Infrared Heating Applications in Food Processing." Molecules 24, no. 22 (November 15, 2019): 4125. http://dx.doi.org/10.3390/molecules24224125.
Повний текст джерелаАнотація:
Infrared (IR) technology is highly energy-efficient, less water-consuming, and environmentally friendly compared to conventional heating. Further, it is also characterized by homogeneity of heating, high heat transfer rate, low heating time, low energy consumption, improved product quality, and food safety. Infrared technology is used in many food manufacturing processes, such as drying, boiling, heating, peeling, polyphenol recovery, freeze-drying, antioxidant recovery, microbiological inhibition, sterilization grains, bread, roasting of food, manufacture of juices, and cooking food. The energy throughput is increased using a combination of microwave heating and IR heating. This combination heats food quickly and eliminates the problem of poor quality. This review provides a theoretical basis for the infrared treatment of food and the interaction of infrared technology with food ingredients. The effect of IR on physico-chemical properties, sensory properties, and nutritional values, as well as the interaction of food components under IR radiation can be discussed as a future food processing option.
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Hussain, G., M. Ranjbar, and S. Hassanzadeh. "Trade-off among mechanical properties and energy consumption in multi-pass friction stir processing of Al7075 alloy employing neural network–based genetic optimization." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 231, no. 1 (August 8, 2016): 129–39. http://dx.doi.org/10.1177/0954405415569817.
Повний текст джерелаАнотація:
Friction stir processing is a novel material processing technique. In this study, neural network–based genetic optimization is applied to optimize the process performance in terms of post-friction stir processing mechanical properties of Al7075 alloy and the energy cost. At first, the experimental data regarding the properties (i.e. elongation, tensile strength and hardness) and the consumed electrical energy are obtained by conducting tests varying two process parameters, namely, feed rate and spindle speed. Then, a numerical model making use of empirical data and artificial neural networks is developed, and multiobjective multivariable genetic optimization is applied to find a trade-off among the performance measures of friction stir processing. For this purpose, the properties like elongation, tensile strength and hardness are maximized and the cost of consumed electrical energy is minimized. Finally, the optimization results are verified by conducting experiments. It is concluded that artificial neural network together with genetic algorithm can be successfully employed to optimize the performance of friction stir processing.
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Wang, Yan, and Na Li. "The Provincial Carbon Footprint and Trade." Advanced Materials Research 524-527 (May 2012): 3514–18. http://dx.doi.org/10.4028/www.scientific.net/amr.524-527.3514.
Повний текст джерелаАнотація:
Based on the data of provincial input-output model and the carbon footprint model, the analysis is focused on provincial carbon footprint and the space transfer of carbon emissions. The results have shown that: (1) There are significant differences of provincial total carbon footprint amounts: resource-rich provinces have high total carbon footprint amounts, followed by processing and manufacturing provinces and municipalities; Regions with high energy efficiency have low carbon footprint amounts, so does southwestern region where economic and industrial development level is relatively low. (2) The provincial differences of carbon footprint per capita are related to demand structure: the amounts of carbon footprint are high in provinces with higher demand of consumption and investment, especially those provinces with strong demand for construction and processing industries. The amounts of carbon footprint are low in provinces which are non-resource-based, have limited investment and construction, and its economic structure is not dominated by processing and manufacturing. (3) Interprovincial trades have a significant impact on carbon footprint and carbon emissions. Provinces with well developed infrastructure have net CO2 emissions flow-in that are directly induced by high energy consumption products; southwestern region, where processing and manufacturing industry is relatively less-developed, has main CO2 emission flow-in, which are induced by the demand of processing and manufacturing industries; resource-intensive provinces and provinces with well-developed processing and manufacturing industries have net CO2 emission flow-out, which are induced by interprovincial trades.
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Rodriguez-Cabal, M. A., Ardila Gonzalo, and Sebastián Rudas. "Prediction of energy consumption in the leadwell v-40 it CNC machining center through artificial neural networks." Journal of Applied Engineering Science 20, no. 1 (2022): 145–49. http://dx.doi.org/10.5937/jaes0-30826.
Повний текст джерелаАнотація:
Energy consumption in machining processes has become a problem for today's manufacturing industry. The use of neural networks and optimization algorithms for modeling and prediction of consumption as a function of the cut-off parameters in processes of this type has aroused the interest of research groups. The present work used artificial neural networks (ANN) to predict the energy consumption of a Leadwell V-40iT® five-axis CNC machining center, based on experimental data obtained through a factorial experimental design 53. ANN was programed in Matlab®. From the study was concluded that the depth per pass (Ap) is the variable that has the most influence on the prediction model of energy consumption with a 77% of relative importance, while the feed rate is the least relevant with 9% of importance.
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Bryntesen, Silje Nornes, Anders Hammer Strømman, Ignat Tolstorebrov, Paul R. Shearing, Jacob J. Lamb, and Odne Stokke Burheim. "Opportunities for the State-of-the-Art Production of LIB Electrodes—A Review." Energies 14, no. 5 (March 4, 2021): 1406. http://dx.doi.org/10.3390/en14051406.
Повний текст джерелаАнотація:
A sustainable shift from internal combustion engine (ICE) vehicles to electric vehicles (EVs) is essential to achieve a considerable reduction in emissions. The production of Li-ion batteries (LIBs) used in EVs is an energy-intensive and costly process. It can also lead to significant embedded emissions depending on the source of energy used. In fact, about 39% of the energy consumption in LIB production is associated with drying processes, where the electrode drying step accounts for about a half. Despite the enormous energy consumption and costs originating from drying processes, they are seldomly researched in the battery industry. Establishing knowledge within the LIB industry regarding state-of-the-art drying techniques and solvent evaporation mechanisms is vital for optimising process conditions, detecting alternative solvent systems, and discovering novel techniques. This review aims to give a summary of the state-of-the-art LIB processing techniques. An in-depth understanding of the influential factors for each manufacturing step of LIBs is then established, emphasising the electrode structure and electrochemical performance. Special attention is dedicated to the convection drying step in conventional water and N-Methyl-2-pyrrolidone (NMP)-based electrode manufacturing. Solvent omission in dry electrode processing substantially lowers the energy demand and allows for a thick, mechanically stable electrode coating. Small changes in the electrode manufacturing route may have an immense impact on the final battery performance. Electrodes used for research and development often have a different production route and techniques compared to those processed in industry. The scalability issues related to the comparison across scales are discussed and further emphasised when the industry moves towards the next-generation techniques. Finally, the critical aspects of the innovations and industrial modifications that aim to overcome the main challenges are presented.