Готові списки джерел за темами / Chemical Process Modeling

Добірка наукової літератури з теми "Chemical Process Modeling"

Автор: Grafiati
Опубліковано: 6 вересня 2023

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Статті в журналах з теми "Chemical Process Modeling"

1

Byun, Ki Ryang, Jeong Won Kang, Ki Oh Song, and Ho Jung Hwang. "Atomic Scale Modeling of Chemical Mechanical Polishing Process." Journal of the Korean Institute of Electrical and Electronic Material Engineers 18, no. 5 (May 1, 2005): 414–22. http://dx.doi.org/10.4313/jkem.2005.18.5.414.

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2

Tiong, Low Soon, and Arshad Ahmad. "A Hybrid Model for Chemical Process Modeling." IFAC Proceedings Volumes 30, no. 25 (September 1997): 163–68. http://dx.doi.org/10.1016/s1474-6670(17)41318-8.

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3

Bhat, N. V., P. A. Minderman, T. McAvoy, and N. S. Wang. "Modeling chemical process systems via neural computation." IEEE Control Systems Magazine 10, no. 3 (April 1990): 24–30. http://dx.doi.org/10.1109/37.55120.

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4

BILIAIEV, М. М., V. V. BILIAIEVA, O. V. BERLOV, V. A. KOZACHYNA, and Z. M. YAKUBOVSKA. "MATHEMATICAL MODELING OF UNSTATIONARY AIR POLLUTION PROCESS." Ukrainian Journal of Civil Engineering and Architecture, no. 3 (015) (June 24, 2023): 13–19. http://dx.doi.org/10.30838/j.bpsacea.2312.140723.13.949.

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Problem statement. The task of determining the dynamics of air pollution in the working room when air containing a chemically hazardous substance flows into it is considered. The peculiarity of this problem is that the formation of pollution areas in the room is influenced by many factors, especially the internal geometry (the presence of technological equipment in the room, furniture, etc.). Therefore, it is necessary to have specialized mathematical models that allow predicting the level of chemical air pollution in the room for a given type of pollution. The purpose of the article. Development of a three-dimensional numerical model for indoor air flow aerodynamics and mass transfer of a chemically hazardous substance entering the room through the ventilation system to predict the risk of toxic damage to workers. Methodology. A three-dimensional equation of convective-diffusion transport for a chemically hazardous substance is used to model the process of a chemically hazardous substance spread in the working room air. The air flow velocity field in the working room is calculated on the basis of the model for the incompressible fluid potential motion. For the numerical integration of the Laplace equation for the velocity potential, two finite-difference schemes are used. The splitting method and finite-difference schemes are used for the numerical integration of the three-dimensional mass transfer equation of the impurity. At each splitting step, the determination of the unknown concentration of the impurity is carried out according to an explicit formula. A computer code was created to conduct computational experiments based on the developed numerical model. Scientific novelty. A three-dimensional numerical model has been developed to analyse the dynamics of the formation of chemical air pollution areas in workplaces when impurities enter the premises through the ventilation system. A feature of the model is the consideration of the main physical factors affecting the formation of pollution areas and the calculation speed. Practical value. The numerical model and the computer code developed on its basis allow solving specific problems that arise when assessing the risk of toxic damage to workers at chemically hazardous facilities. Conclusions. An effective three-dimensional numerical model and computer code have been created, which allow predicting the level of chemical contamination of working premises when a toxic substance enters the premises through the ventilation system. The results of the computational experiment are presented.
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Dong, Gao, Xu Xin, Zhang Beike, Ma Xin, and Wu Chongguang. "A Framework for Agent-based Chemical Process Modeling." Journal of Applied Sciences 13, no. 17 (August 15, 2013): 3490–96. http://dx.doi.org/10.3923/jas.2013.3490.3496.

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Bogomolov, B. B., E. D. Bykov, V. V. Men’shikov, and A. M. Zubarev. "Organizational and technological modeling of chemical process systems." Theoretical Foundations of Chemical Engineering 51, no. 2 (March 2017): 238–46. http://dx.doi.org/10.1134/s0040579517010043.

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Nie, Miaomiao, Jing Tan, Wen-Sheng Deng, and Yue-Feng Su. "Modeling Investigation of Concurrent-flow Chemical Extraction Process." Journal of Physics: Conference Series 1284 (August 2019): 012024. http://dx.doi.org/10.1088/1742-6596/1284/1/012024.

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Gao, Li, and Norman W. Loney. "Evolutionary polymorphic neural network in chemical process modeling." Computers & Chemical Engineering 25, no. 11-12 (November 2001): 1403–10. http://dx.doi.org/10.1016/s0098-1354(01)00708-6.

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Gau, Chao-Yang, and Mark A. Stadtherr. "New interval methodologies for reliable chemical process modeling." Computers & Chemical Engineering 26, no. 6 (June 2002): 827–40. http://dx.doi.org/10.1016/s0098-1354(02)00005-4.

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McBride, Kevin, and Kai Sundmacher. "Overview of Surrogate Modeling in Chemical Process Engineering." Chemie Ingenieur Technik 91, no. 3 (January 3, 2019): 228–39. http://dx.doi.org/10.1002/cite.201800091.

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Дисертації з теми "Chemical Process Modeling"

1

Shi, Ruijie. "Subspace identification methods for process dynamic modeling /." *McMaster only, 2001.

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2

Narisaranukul, Narintr. "Modeling and analysis of the chemical milling process." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/43425.

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Sinangil, Mehmet Selcuk. "Modeling and control on an industrial polymerization process." Thesis, Georgia Institute of Technology, 1995. http://hdl.handle.net/1853/10150.

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4

Johnston, Lloyd Patrick Murphy. "Probability based approaches to process data modeling and rectifictaion." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/10913.

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5

Koulouris, Alexandros. "Multiresolution learning in nonlinear dynamic process modeling and control." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/11376.

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Bohn, Douglas (Douglas Gorman) 1970. "Computer modeling of a continuous manufacturing process." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/47557.

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Анотація:
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management; and, Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1998.
Includes bibliographical references.
by Douglas Bohn.
S.M.
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7

Lai, Jiun-Yu. "Mechanics, mechanisms, and modeling of the chemical mechanical polishing process." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8860.

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Анотація:
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2001.
Includes bibliographical references.
The ever-increasing demand for high-performance microelectronic devices has motivated the semiconductor industry to design and manufacture Ultra-Large-Scale Integrated (ULSI) circuits with smaller feature size, higher resolution, denser packing, and multi-layer interconnects. The ULSI technology places stringent demands on global planarity of the Interlevel Dielectric (ILD) layers. Compared with other planarization techniques, the Chemical Mechanical Polishing (CMP) process produces excellent local and global planarization at low cost. It is thus widely adopted for planarizing inter-level dielectric (silicon dioxide) layers. Moreover, CMP is a critical process for fabricating the Cu damascene patterns, low-k dielectrics, and shallow isolated trenches. The wide range of materials to be polished concurrently or sequentially, however, increases the complexity of CMP and necessitates an understanding of the process fundamentals for optimal process design. This thesis establishes a theoretical framework to relate the process parameters to the different wafer/pad contact modes to study the behavior of wafer-scale polishing. Several models of polishing - microcutting, brittle fracture, surface melting and burnishing - are reviewed. Blanket wafers coated with a wide range of materials are polished to verify the models. Plastic deformation is identified as the dominant mechanism of material removal in fine abrasive polishing.
(cont.) Additionally, contact mechanics models, which relate the pressure distribution to the pattern geometry and pad elastic properties, explain the die-scale variation of material removal rate (MRR) on pattern geometry. The pad displacement into low features of submicron lines is less than 0.1 nm. Hence the applied load is only carried by the high features, and the pressure on high features increases with the area fraction of interconnects. Experiments study the effects of pattern geometry on the rates of pattern planarization, oxide overpolishing and Cu dishing. It was observed that Cu dishing of submicron features is less than 20 nm and contributes less to surface non-uniformity than does oxide overpolishing. Finally, a novel in situ detection technique, based on the change of the reflectance of the patterned surface at different polishing stages, is developed to detect the process endpoint and minimize overpolishing. Models that employ light scattering theory and statistical treatment correlate the sampled reflectance with the surface topography and Cu area fraction for detecting the process regime and endpoint. The experimental results agree well with the endpoint detection schemes predicted by the models.
by Jiun-Yu Lai.
Ph.D.
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8

Bakshi, Bhavik Ramesh. "Multi-resolution methods for modeling, analysis and control of chemical process operations." Thesis, Massachusetts Institute of Technology, 1992. http://hdl.handle.net/1721.1/13203.

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Bryden, Michelle D. (Michelle Denise). "Macrotransport process in branching networks : modeling convective-diffusive phenomena in the lung." Thesis, Massachusetts Institute of Technology, 1994. http://hdl.handle.net/1721.1/33514.

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Ji, Qingjun. "Mathematical modeling of carbon black process from coal." Ohio : Ohio University, 2000. http://www.ohiolink.edu/etd/view.cgi?ohiou1172255200.

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Книги з теми "Chemical Process Modeling"

1

Denn, Morton M. Process modeling. New York: Longman, 1986.

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2

Process modeling. Harlow: Longman Scientific & Technical, 1987.

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3

Georgiadis, Michael C., Julio R. Banga, and Efstratios N. Pistikopoulos. Dynamic process modeling. Weinheim: Wiley-VCH, 2011.

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4

Process dynamics: Modeling, analysis, and simulation. Upper Saddle River, N.J: Prentice Hall PTR, 1998.

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5

Georgiadis, Michael C. Dynamic process modeling. Weinheim: Wiley-VCH, 2011.

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6

1940-, Ray W. Harmon, ed. Process dynamics, modeling, and control. New York: Oxford University Press, 1994.

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7

Process modeling, simulation, and control for chemical engineers. 2nd ed. New York: McGraw-Hill, 1990.

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8

E, Schiesser W., ed. Dynamic modeling of transport process systems. San Diego: Academic Press, 1992.

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9

Upreti, Simant Ranjan. Process Modeling and Simulation for Chemical Engineers. Chichester, UK: John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118914670.

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10

Ghasem, Nayef. Modeling and Simulation of Chemical Process Systems. Boca Raton, FL : CRC Press/Taylor & Francis Group, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/b22487.

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Частини книг з теми "Chemical Process Modeling"

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am Ende, Mary T., William Ketterhagen, Andrew Prpich, Pankaj Doshi, Salvador García-Muñoz, and Rahul Bharadwajh. "DRUG PRODUCT PROCESS MODELING." In Chemical Engineering in the Pharmaceutical Industry, 489–525. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119600800.ch70.

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2

Sharma, Shivom, and G. P. Rangaiah. "Mathematical Modeling, Simulation and Optimization for Process Design." In Chemical Process Retrofitting and Revamping, 97–127. Chichester, UK: John Wiley & Sons, Ltd, 2016. http://dx.doi.org/10.1002/9781119016311.ch4.

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3

Simpson, Ricardo, and Sudhir K. Sastry. "Fundamentals of Mathematical Modeling, Simulation, and Process Control." In Chemical and Bioprocess Engineering, 245–60. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-9126-2_9.

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4

Chen, Chau-Chyun. "Molecular Thermodynamics for Pharmaceutical Process Modeling and Simulation." In Chemical Engineering in the Pharmaceutical Industry, 505–19. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470882221.ch27.

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Ghasem, Nayef. "Introduction." In Modeling and Simulation of Chemical Process Systems, 1–38. Boca Raton, FL : CRC Press/Taylor & Francis Group, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/b22487-1.

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Ghasem, Nayef. "Lumped Parameter Systems." In Modeling and Simulation of Chemical Process Systems, 39–105. Boca Raton, FL : CRC Press/Taylor & Francis Group, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/b22487-2.

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Ghasem, Nayef. "Theory and Applications of Distributed Systems." In Modeling and Simulation of Chemical Process Systems, 107–53. Boca Raton, FL : CRC Press/Taylor & Francis Group, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/b22487-3.

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Ghasem, Nayef. "Computational Fluid Dynamics." In Modeling and Simulation of Chemical Process Systems, 155–221. Boca Raton, FL : CRC Press/Taylor & Francis Group, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/b22487-4.

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Ghasem, Nayef. "Mass Transport of Distributed Systems." In Modeling and Simulation of Chemical Process Systems, 223–72. Boca Raton, FL : CRC Press/Taylor & Francis Group, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/b22487-5.

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Ghasem, Nayef. "Heat Transfer Distributed Parameter Systems." In Modeling and Simulation of Chemical Process Systems, 273–361. Boca Raton, FL : CRC Press/Taylor & Francis Group, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/b22487-6.

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Тези доповідей конференцій з теми "Chemical Process Modeling"

1

Baharev, Ali, and Arnold Neumaier. "Chemical Process Modeling in Modelica." In 9th International MODELICA Conference, Munich, Germany. Linköping University Electronic Press, 2012. http://dx.doi.org/10.3384/ecp12076955.

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2

R. Stoyanov, Stanislav, and Andriy Kovalenko. "Multiscale Computational Modeling: From Heavy Petroleum to Biomass Valorization." In Annual International Conference on Chemistry, Chemical Engineering and Chemical Process. Global Science & Technology Forum (GSTF), 2015. http://dx.doi.org/10.5176/2301-3761_ccecp15.48.

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3

Yu, Wen, and Francisco J. Pineda. "Chemical process modeling with multiple neural networks." In 2001 European Control Conference (ECC). IEEE, 2001. http://dx.doi.org/10.23919/ecc.2001.7076515.

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4

Kamchaddaskorn, Atthadej, Nalinee Mukdasanit, and Thongchai Srinophakun. "Process Modeling and Simulation of Cyclohexanone Production." In The 3rd World Congress on Mechanical, Chemical, and Material Engineering. Avestia Publishing, 2017. http://dx.doi.org/10.11159/iccpe17.118.

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5

Lin, Po Ting, Yogesh Jaluria, and Hae Chang Gea. "Parametric Modeling and Optimization of Chemical Vapor Deposition Process." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-50054.

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This paper focuses on the parametric modeling and optimization of the Chemical Vapor Deposition (CVD) process for the deposition of thin films of silicon from silane in a vertical impinging CVD reactor. The parametric modeling using Radial Basis Function (RBF) for various functions which are related to the deposition rate and uniformity of the thin films are studied. These models are compared and validated with additional sampling data. Based on the parametric models, different optimization formulations for maximizing the deposition rate and the working areas of thin film are performed.
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6

Zographos, Nikolas, Christoph Zechner, Pedro Castrillo, and Ignacio Martin-Bragado. "Process modeling of chemical and stress effects in SiGe." In ION IMPLANTATION TECHNOLOGY 2012: Proceedings of the 19th International Conference on Ion Implantation Technology. AIP, 2012. http://dx.doi.org/10.1063/1.4766526.

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7

Chojnowski, Krystian, Piotr Wasilewski, and Rafał Grądzki. "Movement modeling of mobile robot in MegaSumo category." In 2ND INTERNATIONAL CONFERENCE ON CHEMISTRY, CHEMICAL PROCESS AND ENGINEERING (IC3PE). Author(s), 2018. http://dx.doi.org/10.1063/1.5066472.

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Shao, Hua, Panpan Lai, Junjie Li, Guobin Bai, Qi Yan, Junfeng Li, Tao Yang, Rui Chen, and Yayi Wei. "Modeling of SiNx growth by chemical vapor deposition in nanosheet indentation." In Advanced Etch Technology and Process Integration for Nanopatterning XII, edited by Efrain Altamirano-Sánchez and Nihar Mohanty. SPIE, 2023. http://dx.doi.org/10.1117/12.2658152.

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"Computational Fluid Dynamics Modeling of Mixing Process for Two-Components Mixture in the Large Scale Reactor." In Chemical technology and engineering. Lviv Polytechnic National University, 2021. http://dx.doi.org/10.23939/cte2021.01.038.

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10

Rao, S. Rama, C. R. M. Sravan, V. Pandu Ranga, and G. Padmanabhan. "Fuzzy logic-based forward modeling of Electro Chemical Machining process." In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE, 2009. http://dx.doi.org/10.1109/nabic.2009.5393708.

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Звіти організацій з теми "Chemical Process Modeling"

1

Martino, C., D. Herman, J. Pike, and T. Peters. ACTINIDE REMOVAL PROCESS SAMPLE ANALYSIS, CHEMICAL MODELING, AND FILTRATION EVALUATION. Office of Scientific and Technical Information (OSTI), June 2014. http://dx.doi.org/10.2172/1134065.

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2

Xu, Dikai, Yu-Yen Chen, Jianhua Pan, Yitao Zhang, Dawei Wang, Yaswanth Pottimurthy, Thomas J. Flynn, et al. Heat Integration Optimization and Dynamic Modeling Investigation for Advancing the Coal-Direct Chemical Looping Process. Office of Scientific and Technical Information (OSTI), April 2020. http://dx.doi.org/10.2172/1608820.

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Nechypurenko, Pavlo, Tetiana Selivanova, and Maryna Chernova. Using the Cloud-Oriented Virtual Chemical Laboratory VLab in Teaching the Solution of Experimental Problems in Chemistry of 9th Grade Students. [б. в.], June 2019. http://dx.doi.org/10.31812/123456789/3175.

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The article discusses the importance of the skills of primary school students to solve experimental problems in chemistry and the conditions for the use of virtual chemical laboratories in the process of the formation of these skills. The concept of “experimental chemical problem” was analyzed, classifications were considered, and methodological conditions for using experimental chemical problems in the process of teaching chemistry were described. The essence of the concept of “virtual chemical laboratories” is considered and their main types, advantages and disadvantages that define the methodically reasonable limits of the use of these software products in the process of teaching chemistry, in particular, to support the educational chemical experiment are described. The capabilities of the virtual chemical laboratory VLab to support the process of solving experimental problems in chemistry in grade 9 have been determined. The main advantages and disadvantages of the virtual chemical laboratory VLab on the modeling of chemical processes necessary for the creation of virtual experimental problems in chemistry are analyzed. The features of the virtual chemical laboratory VLab, the essence of its work and the creation of virtual laboratory work in it are described. The results of the study is the development of a set of experimental tasks in chemistry for students in grade 9 on the topic “Solutions” in the cloud-oriented virtual chemical laboratory VLab.
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4

Morkun, Volodymyr, Natalia Morkun, Andrii Pikilnyak, Serhii Semerikov, Oleksandra Serdiuk, and Irina Gaponenko. The Cyber-Physical System for Increasing the Efficiency of the Iron Ore Desliming Process. CEUR Workshop Proceedings, April 2021. http://dx.doi.org/10.31812/123456789/4373.

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It is proposed to carry out the spatial effect of high-energy ultrasound dynamic effects with controlled characteristics on the solid phase particles of the ore pulp in the deslimer input product to increase the efficiency of thickening and desliming processes of iron ore beneficiation products. The above allows predicting the characteristics of particle gravitational sedimentation based on an assessment of the spatial dynamics of pulp solid- phase particles under the controlled action of high-energy ultrasound and fuzzy logical inference. The object of study is the assessment of the characteristics and the process of control the operations of thickening and deslaming of iron ore beneficiation products in the conditions of the technological line of the ore beneficiation plant. The subject of study is a cyber-physical system based on the use of high-energy ultrasound radiation pressure effects on iron-containing beneficiation products in the technological processes of thickening and desliming. The working hypothesis of the project is that there is a relationship between the physical-mechanical and chemical-mineralogical characteristics of the iron ore pulp solid- phase particles and their behavior in technological flows under the influence of controlled ultrasonic radiation, based on which the imitation modeling of the gravitational sedimentation process of the iron ore pulp solid-phase particles can be performed directly in the technological process. Also, the optimal control actions concerning the processes of thickening and desliming can be determined.
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Seale, Maria, R. Salter, Natàlia Garcia-Reyero,, and Alicia Ruvinsky. A fuzzy epigenetic model for representing degradation in engineered systems. Engineer Research and Development Center (U.S.), September 2022. http://dx.doi.org/10.21079/11681/45582.

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Degradation processes are implicated in a large number of system failures, and are crucial to understanding issues related to reliability and safety. Systems typically degrade in response to stressors, such as physical or chemical environmental conditions, which can vary widely for identical units that are deployed in different places or for different uses. This situational variance makes it difficult to develop accurate physics-based or data-driven models to assess and predict the system health status of individual components. To address this issue, we propose a fuzzy set model for representing degradation in engineered systems that is based on a bioinspired concept from the field of epigenetics. Epigenetics is concerned with the regulation of gene expression resulting from environmental or other factors, such as toxicants or diet. One of the most studied epigenetic processes is methylation, which involves the attachment of methyl groups to genomic regulatory regions. Methylation of specific genes has been implicated in numerous chronic diseases, so provides an excellent analog to system degradation. We present a fuzzy set model for characterizing system degradation as a methylation process based on a set-theoretic representation for epigenetic modeling of engineered systems. This model allows us to capture the individual dynamic relationships among a system, environmental factors, and state of health.
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Banks, H. T. Modeling Validation and Control of Advanced Chemical Vapor Deposition Processes. Fort Belvoir, VA: Defense Technical Information Center, November 2000. http://dx.doi.org/10.21236/ada384359.

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Mojdeh Delshad, Gary A. Pope, and Kamy Sepehrnoori. Modeling Wettability Alteration using Chemical EOR Processes in Naturally Fractured Reservoirs. Office of Scientific and Technical Information (OSTI), September 2007. http://dx.doi.org/10.2172/927590.

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Zhylenko, Tetyana I., Ivan S. Koziy, Vladyslav S. Bozhenko, and Irina A. Shuda. Using a web application to realize the effect of AR in assessing the environmental impact of emissions source. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4408.

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Revolutionary technologies of nowadays are virtual and augmented reality. Humanity's concern for nature may be affected by their ability to combine reality with the simulated effects of human impact on the environment. An urgent task today is creating software applications to assess the impact of human activities on the environment. Recently, most scientists have been trying to model the impact of various factors on environmental change today and for decades using information technology. Visual models are very impressive and they also make a deep impression on the psychological state of the person. This forces people to use natural resources wisely. In this article we have considered the sequential process of building and implementing models for assessing the impact of pollutants from a stationary emission source. We have created a software product that helps to show visually how the emissions of a chemical plant are spreading to the surrounding city. The harmfulness to the city of the cloud into which emissions are converted can also be calculated by the program. We have implemented a number of functions responsible for emission modeling, taking into account different conditions.
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Ersoy, Daniel. 693JK31810003 Non-Destructive Tools for Surface to Bulk Correlations of Yield Strength Toughness and Chemistry. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), February 2022. http://dx.doi.org/10.55274/r0012206.

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Evaluates the use of non-destructive surface testing (micro indentation, micro-machining, in situ chemistry, and replicate microscopy analysis) as a means to perform pipe material confirmation. The test results from thousands of lab and field material tests done on actual pipeline samples have been used to develop models that account for pipe material thermo-mechanical process variations and through-wall variability of material, mechanical, and chemical properties. A separate "training set" of twenty pipelines was made available to GTI, Element Resources, and ASU to allow initial model testing and prove-out prior to the seventy primary samples that were used to fully characterize pipeline properties and the correlation of surface to bulk properties, as well as develop predictive models of bulk properties based solely on surface obtained pipeline data. A set of seventy pipeline samples (termed Pipe Library) that were in service from the natural gas industry were selected for the project testing and modeling. A great deal of care and effort was put forth to select a reasonable number that provided the adequate breadth of variety as typically encountered by the industry in the field.
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You, Siming, Ondřej Mašek, Bauyrzhan Biakhmetov, Simon Ascher, Sudeshna Lahiri, PreetiChaturvedi Bhargava, Thallada Bhaskar, Supravat Sarangi, and Sunita Varjani. Feasibility and impacts of Bioenergy Trigeneration systems (BioTrig) in disadvantaged rural areas in India. University of Glasgow, August 2023. http://dx.doi.org/10.36399/gla.pubs.305660.

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This project aims to evaluate the techno-economic and social feasibility, and socio-environmental impacts of bioenergy trigeneration (electricity, clean cooking fuel, and green soil conditioner) systems that tackle the triple crisis of poor electrification, household air pollution, and farmland contamination in rural India. This system is called BioTrig. A project workshop has been held in November in India to discuss and finalise action plans. A questionnaire has been developed to understand the energy, resource, and new technology acceptance of rural households in India. Chemical process modelling, life cycle assessment and cost-benefit analysis has been conducted to evaluate the environmental impact and economic feasibility of BioTrig.
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