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Journal articles on the topic 'Automatic distillation of structure'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Luo, Ling, Dingyu Xue, and Xinglong Feng. "Automatic Diabetic Retinopathy Grading via Self-Knowledge Distillation." Electronics 9, no. 9 (August 19, 2020): 1337. http://dx.doi.org/10.3390/electronics9091337.

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Diabetic retinopathy (DR) is a common fundus disease that leads to irreversible blindness, which plagues the working-age population. Automatic medical imaging diagnosis provides a non-invasive method to assist ophthalmologists in timely screening of suspected DR cases, which prevents its further deterioration. However, the state-of-the-art deep-learning-based methods generally have a large amount of model parameters, which makes large-scale clinical deployment a time-consuming task. Moreover, the severity of DR is associated with lesions, and it is difficult for the model to focus on these regions. In this paper, we propose a novel deep-learning technique for grading DR with only image-level supervision. Specifically, we first customize the model with the help of self-knowledge distillation to achieve a trade-off between model performance and time complexity. Secondly, CAM-Attention is used to allow the network to focus on discriminative zone, e.g., microaneurysms, soft/hard exudates, etc.. Considering that directly attaching a classifier after the Side branch will disrupt the hierarchical nature of convolutional neural networks, a Mimicking Module is employed that allows the Side branch to actively mimic the main branch structure. Extensive experiments are conducted on two benchmark datasets, with an AUC of 0.965 and an accuracy of 92.9% for the Messidor dataset and 67.96% accuracy achieved for the challenging IDRID dataset, which demonstrates the superior performance of our proposed method.

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Liu, Ning, Yan Li, and Wan Jie Sun. "Study on Plate Washer Sewage Treatment." Applied Mechanics and Materials 312 (February 2013): 546–49. http://dx.doi.org/10.4028/www.scientific.net/amm.312.546.

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To study the plate washer sewage treatment, the mature theories and structures in other sectors were introduced the printing industry. Plate washer is a kind of printing auxiliary equipment, mainly used for the printing plate cleaning and washing wastewater treating and recycling, the sewage treatment system including filtration part and distillation part, filtering part which is based on design of automatic self-cleaning filter have two stage filtration system, distillation part use crude oil distillation tower as the distillation system model.

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3

Sutijan, Sutijan, Megan Jobson, and Robin Smith. "Synthesis of Ternary Homogeneous Azeotropic Distillation Sequences: Entrainer Selection." ASEAN Journal of Chemical Engineering 12, no. 1 (August 6, 2012): 20. http://dx.doi.org/10.22146/ajche.49752.

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This paper presents a methodology for automatic selection of entrainers for separating binary azeotropic mixtures using homogeneous azeotropic distillation. A new classification system for ternary mixtures based on the termini of distillation boundaries and the type (stability) of products and azeotropes is proposed. The new characterisation system is able to link candidate entrainers to flowsheet structures which can facilitate the separation. Existing entrainer selection criteria are extended to accommodate other promising entrainers, including light, intermediate and heavy-boiling entrainers.

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4

Musa, Aminu, Mohammed Hassan, Mohamed Hamada, and Farouq Aliyu. "Low-Power Deep Learning Model for Plant Disease Detection for Smart-Hydroponics Using Knowledge Distillation Techniques." Journal of Low Power Electronics and Applications 12, no. 2 (April 26, 2022): 24. http://dx.doi.org/10.3390/jlpea12020024.

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Recent advances in computing allows researchers to propose the automation of hydroponic systems to boost efficiency and reduce manpower demands, hence increasing agricultural produce and profit. A completely automated hydroponic system should be equipped with tools capable of detecting plant diseases in real-time. Despite the availability of deep-learning-based plant disease detection models, the existing models are not designed for an embedded system environment, and the models cannot realistically be deployed on resource-constrained IoT devices such as raspberry pi or a smartphone. Some of the drawbacks of the existing models are the following: high computational resource requirements, high power consumption, dissipates energy rapidly, and occupies large storage space due to large complex structure. Therefore, in this paper, we proposed a low-power deep learning model for plant disease detection using knowledge distillation techniques. The proposed low-power model has a simple network structure of a shallow neural network. The parameters of the model were also reduced by more than 90%. This reduces its computational requirements as well as its power consumption. The proposed low-power model has a maximum power consumption of 6.22 w, which is significantly lower compared to the existing models, and achieved a detection accuracy of 99.4%.

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Musa, Aminu, Mohammed Hassan, Mohamed Hamada, and Farouq Aliyu. "Low-Power Deep Learning Model for Plant Disease Detection for Smart-Hydroponics Using Knowledge Distillation Techniques." Journal of Low Power Electronics and Applications 12, no. 2 (April 26, 2022): 24. http://dx.doi.org/10.3390/jlpea12020024.

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Recent advances in computing allows researchers to propose the automation of hydroponic systems to boost efficiency and reduce manpower demands, hence increasing agricultural produce and profit. A completely automated hydroponic system should be equipped with tools capable of detecting plant diseases in real-time. Despite the availability of deep-learning-based plant disease detection models, the existing models are not designed for an embedded system environment, and the models cannot realistically be deployed on resource-constrained IoT devices such as raspberry pi or a smartphone. Some of the drawbacks of the existing models are the following: high computational resource requirements, high power consumption, dissipates energy rapidly, and occupies large storage space due to large complex structure. Therefore, in this paper, we proposed a low-power deep learning model for plant disease detection using knowledge distillation techniques. The proposed low-power model has a simple network structure of a shallow neural network. The parameters of the model were also reduced by more than 90%. This reduces its computational requirements as well as its power consumption. The proposed low-power model has a maximum power consumption of 6.22 w, which is significantly lower compared to the existing models, and achieved a detection accuracy of 99.4%.

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6

Musa, Aminu, Mohammed Hassan, Mohamed Hamada, and Farouq Aliyu. "Low-Power Deep Learning Model for Plant Disease Detection for Smart-Hydroponics Using Knowledge Distillation Techniques." Journal of Low Power Electronics and Applications 12, no. 2 (April 26, 2022): 24. http://dx.doi.org/10.3390/jlpea12020024.

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Recent advances in computing allows researchers to propose the automation of hydroponic systems to boost efficiency and reduce manpower demands, hence increasing agricultural produce and profit. A completely automated hydroponic system should be equipped with tools capable of detecting plant diseases in real-time. Despite the availability of deep-learning-based plant disease detection models, the existing models are not designed for an embedded system environment, and the models cannot realistically be deployed on resource-constrained IoT devices such as raspberry pi or a smartphone. Some of the drawbacks of the existing models are the following: high computational resource requirements, high power consumption, dissipates energy rapidly, and occupies large storage space due to large complex structure. Therefore, in this paper, we proposed a low-power deep learning model for plant disease detection using knowledge distillation techniques. The proposed low-power model has a simple network structure of a shallow neural network. The parameters of the model were also reduced by more than 90%. This reduces its computational requirements as well as its power consumption. The proposed low-power model has a maximum power consumption of 6.22 w, which is significantly lower compared to the existing models, and achieved a detection accuracy of 99.4%.

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7

Xiao, Da, Tianyu Huang, Yihao Li, Chang Liu, and Fuquan Zhang. "A Lightweight Human Action Classification Method for Green IoT Sport Applications." Journal of Sensors 2022 (July 1, 2022): 1–16. http://dx.doi.org/10.1155/2022/4102552.

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This paper proposes a lightweight human action classification method for Green Internet of Things (IoT) sport applications. This method classifies the human motion data collected by wearables or other IoT devices with energy-efficient techniques, by enabling a small number of sample training and incremental classification to achieve the purpose of energy-efficient. To lessen the complexity of the model and reduce the number of samples required for parameter estimation, we propose a shared Hidden Conditional Random Field (sHCRF) model. The sHCRF model adds a shared-classification layer structure to reduce the parameter computation. In the experiments, the classification accuracy of the sHCRF model is above 95%. This paper introduces an incremental learning method based on knowledge distillation. The new model suppresses the forgetting of existing classification knowledge while fitting new data to learn new classification knowledge. In the incremental scenarios, the classification accuracy of the sHCRF model is above 70%. The experimental results show that this method can lightly implement convenient and fast automatic classification of action acquisition.

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8

Sivananaithaperumal, Sudalaiandi, and Subramanian Baskar. "Design Of Multivariable Fractional Order Pid Controller Using Covariance Matrix Adaptation Evolution Strategy." Archives of Control Sciences 24, no. 2 (June 1, 2014): 235–51. http://dx.doi.org/10.2478/acsc-2014-0014.

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Abstract This paper presents an automatic tuning of multivariable Fractional-Order Proportional, Integral and Derivative controller (FO-PID) parameters using Covariance Matrix Adaptation Evolution Strategy (CMAES) algorithm. Decoupled multivariable FO-PI and FO-PID controller structures are considered. Oustaloup integer order approximation is used for the fractional integrals and derivatives. For validation, two Multi-Input Multi- Output (MIMO) distillation columns described byWood and Berry and Ogunnaike and Ray are considered for the design of multivariable FO-PID controller. Optimal FO-PID controller is designed by minimizing Integral Absolute Error (IAE) as objective function. The results of previously reported PI/PID controller are considered for comparison purposes. Simulation results reveal that the performance of FOPI and FO-PID controller is better than integer order PI/PID controller in terms of IAE. Also, CMAES algorithm is suitable for the design of FO-PI / FO-PID controller.

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Cai, Qiwen, Ran Chen, Lu Li, Chao Huang, Haisu Pang, Yuanshi Tian, Min Di, et al. "The Application of Knowledge Distillation toward Fine-Grained Segmentation for Three-Vessel View of Fetal Heart Ultrasound Images." Computational Intelligence and Neuroscience 2022 (July 14, 2022): 1–7. http://dx.doi.org/10.1155/2022/1765550.

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Objectives. Measuring anatomical parameters in fetal heart ultrasound images is crucial for the diagnosis of congenital heart disease (CHD), which is highly dependent on the clinical experience of the sonographer. To address this challenge, we propose an automated segmentation method using the channel-wise knowledge distillation technique. Methods. We design a teacher-student architecture to conduct channel-wise knowledge distillation. ROI-based cropped images and full-size images are used for the teacher and student models, respectively. It allows the student model to have both the fine-grained segmentation capability inherited from the teacher model and the ability to handle full-size test images. A total of 1,300 fetal heart ultrasound images of three-vessel view were collected and annotated by experienced doctors for training, validation, and testing. Results. We use three evaluation protocols to quantitatively evaluate the segmentation accuracy: Intersection over Union (IoU), Pixel Accuracy (PA), and Dice coefficient (Dice). We achieved better results than related methods on all evaluation metrics. In comparison with DeepLabv3+, the proposed method gets more accurate segmentation boundaries and has performance gains of 1.8% on mean IoU (66.8% to 68.6%), 2.2% on mean PA (79.2% to 81.4%), and 1.2% on mean Dice (80.1% to 81.3%). Conclusions. Our segmentation method could identify the anatomical structure in three-vessel view of fetal heart ultrasound images. Both quantitative and visual analyses show that the proposed method significantly outperforms the related methods in terms of segmentation results.

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Krasikova, Raisa N., and Viktoriya V. Orlovskaya. "Phase Transfer Catalysts and Role of Reaction Environment in Nucleophilc Radiofluorinations in Automated Synthesizers." Applied Sciences 12, no. 1 (December 29, 2021): 321. http://dx.doi.org/10.3390/app12010321.

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Incorporation of [18F]fluorine into PET radiotracer structure has traditionally been accomplished via nucleophilic pathways. The [18F]fluoride is generated in an aqueous solution via proton irradiation of oxygen-18 enriched water and must to be introduced into water-free organic solutions in order to generate reactive species. Thus nucleophilic 18F-fluorination traditionally included steps for [18F]fluoride concentration on the anion exchange resin, followed by removal of residual water via azeotropic distillation with MeCN, a time-consuming process associated with radioactivity losses and difficult automation. To circumvent this, several adsorption/elution protocols were developed based on the minimization of water content in traditional kryptofix-based [18F]fluoride eluents. The use of pre-dried KOH/kryptofix solutions, tertiary alcohols, and strong organic bases was found to be effective. Advances in transition metal-mediated SNAr approaches for radiolabeling of non-activated aromatic substrates have prompted development of alternative techniques for reactive [18F]fluoride species generation, such as organic solutions of non-basic alkyl ammonium and pyridinium sulfonates, etc. For radiofluorinations of iodonium salts precursors, a “minimalist” approach was introduced, avoiding the majority of pitfalls common to more complex methods. These innovations allowed the development of new time-efficient and convenient work-up procedures that are easily implementable in modern automated synthesizers. They will be the subject of this review.

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11

Wang, Jiacheng, Xiaomeng Li, Yiming Han, Jing Qin, Liansheng Wang, and Zhou Qichao. "Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (June 28, 2022): 2459–67. http://dx.doi.org/10.1609/aaai.v36i3.20146.

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Automatic delineation of organ-at-risk (OAR) and gross-tumor-volume (GTV) is of great significance for radiotherapy planning. However, it is a challenging task to learn powerful representations for accurate delineation under limited pixel (voxel)-wise annotations. Contrastive learning at pixel-level can alleviate the dependency on annotations by learning dense representations from unlabeled data. Recent studies in this direction design various contrastive losses on the feature maps, to yield discriminative features for each pixel in the map. However, pixels in the same map inevitably share semantics to be closer than they actually are, which may affect the discrimination of pixels in the same map and lead to the unfair comparison to pixels in other maps. To address these issues, we propose a separated region-level contrastive learning scheme, namely SepaReg, the core of which is to separate each image into regions and encode each region separately. Specifically, SepaReg comprises two components: a structure-aware image separation (SIS) module and an intra- and inter-organ distillation (IID) module. The SIS is proposed to operate on the image set to rebuild a region set under the guidance of structural information. The inter-organ representation will be learned from this set via typical contrastive losses cross regions. On the other hand, the IID is proposed to tackle the quantity imbalance in the region set as tiny organs may produce fewer regions, by exploiting intra-organ representations. We conducted extensive experiments to evaluate the proposed model on a public dataset and two private datasets. The experimental results demonstrate the effectiveness of the proposed model, consistently achieving better performance than state-of-the-art approaches. Code is available at https://github.com/jcwang123/Separate_CL.

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12

Ling, Xufeng, Yapeng Wu, Rahman Ali, and Huaizhong Zhu. "Magnetic Tile Surface Defect Detection Methodology Based on Self-Attention and Self-Supervised Learning." Computational Intelligence and Neuroscience 2022 (August 3, 2022): 1–10. http://dx.doi.org/10.1155/2022/3003810.

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As the core component of permanent magnet motor, the magnetic tile defects seriously affect the quality of industrial motor. Automatic recognition of the surface defects of the magnetic tile is a difficult job since the patterns of the defects are complex and diverse. The existing defect recognition methods result in difficulty in practical application due to the complicated system structure and the low accuracy of the image segmentation and the target detection for the diversity of the defect patterns. A self-supervised learning (SSL) method, which benefits from its nonlinear feature extraction performance, is proposed in this study to improve the existing approaches. We proposed an efficient multihead self-attention method, which can automatically locate single or multiple defect areas of magnetic tile and extract features of the magnetic tile defects. We also designed an accurate full-connection classifier, which can accurately classify different defects of magnetic tile defects. A knowledge distillation process without labeling is proposed, which simplifies the self-supervised training process. The process of our method is as follows. A feature extraction model consists of standard vision transformer (ViT) backbone, which is trained by contrast learning without labeled dataset that is used to extract global and local features from the input magnetic tile images. Then, we use a full-connection neural network, which is trained by using labeled dataset to classify the known defect types. Finally, we combined the feature extraction model and defect classification model together to form a relatively simple integrated system. The public magnetic tile surface defect dataset, which holds 5 defect categories and 1 nondefect category, is used in the process of training, validating, and testing. We also use online data augmentation techs to increase training samples to make the model converge and achieve high classification accuracy. The experimental results show that the features extracted by the SSL method can get richer and more detailed features than the supervised learning model gets. The composite model reaches to a high testing accuracy of 98.3%, and gains relatively strong robustness and good generalization ability.

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13

Weng, Yongbiao, Aina Johannessen, and Harald Sodemann. "High-resolution stable isotope signature of a land-falling atmospheric river in southern Norway." Weather and Climate Dynamics 2, no. 3 (August 5, 2021): 713–37. http://dx.doi.org/10.5194/wcd-2-713-2021.

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Abstract. Heavy precipitation at the west coast of Norway is often connected to elongated meridional structures of high integrated water vapour transport known as atmospheric rivers (ARs). Here we present high-resolution measurements of stable isotopes in near-surface water vapour and precipitation during a land-falling AR in southwestern Norway on 7 December 2016. In our analysis, we aim to identify the influences of moisture source conditions, weather system characteristics, and post-condensation processes on the isotope signal in near-surface water vapour and precipitation. A total of 71 precipitation samples were collected during the 24 h sampling period, mostly taken at sampling intervals of 10–20 min. The isotope composition of near-surface vapour was continuously monitored in situ with a cavity ring-down spectrometer. Local meteorological conditions were in addition observed from a vertical pointing rain radar, a laser disdrometer, and automatic weather stations. We observe a stretched, “W”-shaped evolution of isotope composition during the event. Combining paired precipitation and vapour isotopes with meteorological observations, we define four different stages of the event. The two most depleted periods in the isotope δ values are associated with frontal transitions, namely a combination of two warm fronts that follow each other within a few hours and an upper-level cold front. The d-excess shows a single maximum and a step-wise decline in precipitation and a gradual decrease in near-surface vapour. Thereby, the isotopic evolution of the near-surface vapour closely follows that of the precipitation with a time delay of about 30 min, except for the first stage of the event. Analysis using an isotopic below-cloud exchange framework shows that the initial period of low and even negative d-excess in precipitation was caused by evaporation below cloud base. The isotope signal from the cloud level became apparent at ground level after a transition period that lasted up to several hours. Moisture source diagnostics for the periods when the cloud signal dominates show that the moisture source conditions are then partly reflected in surface precipitation and water vapour isotopes. In our study, the isotope signal in surface precipitation during the AR event reflects the combined influence of atmospheric dynamics, moisture sources, and atmospheric distillation, as well as cloud microphysics and below-cloud processes. Based on this finding, we recommend careful interpretation of results obtained from Rayleigh distillation models in such events, in particular for the interpretation of surface vapour and precipitation from stratiform clouds.

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Paler, Alexandru, Austin G. Fowler, and Robert Wille. "Online scheduled execution of quantum circuits protected by surface codes." Quantum Information and Computation 17, no. 15&16 (December 2017): 1335–48. http://dx.doi.org/10.26421/qic17.15-16-5.

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Quantum circuits are the preferred formalism for expressing quantum information processing tasks. Quantum circuit design automation methods mostly use a waterfall approach and consider that high level circuit descriptions are hardware agnostic. This assumption has lead to a static circuit perspective: the number of quantum bits and quantum gates is determined before circuit execution and everything is considered reliable with zero probability of failure. Many different schemes for achieving reliable fault-tolerant quantum computation exist, with different schemes suitable for different architectures. A number of large experimental groups are developing architectures well suited to being protected by surface quantum error correcting codes. Such circuits could include unreliable logical elements, such as state distillation, whose failure can be determined only after their actual execution. Therefore, practical logical circuits, as envisaged by many groups, are likely to have a dynamic structure. This requires an online scheduling of their execution: one knows for sure what needs to be executed only after previous elements have finished executing. This work shows that scheduling shares similarities with place and route methods. The work also introduces the first online schedulers of quantum circuits protected by surface codes. The work also highlights scheduling efficiency by comparing the new methods with state of the art static scheduling of surface code protected fault-tolerant circuits.

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Diaz Quezada, Simon, Jose Ricardo Perez Correa, and Mario A. Fernandez Fernandez. "Automatic System Distillation for Wine Fruit." IEEE Latin America Transactions 13, no. 6 (June 2015): 1882–87. http://dx.doi.org/10.1109/tla.2015.7164212.

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Yorke, S. G. "An improved automatic mercury-distillation apparatus." Journal of Applied Chemistry 2, no. 2 (May 4, 2007): 77–79. http://dx.doi.org/10.1002/jctb.5010020206.

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Ruekberg, Ben. "An automatic water trap for steam distillation." Journal of Chemical Education 67, no. 1 (January 1990): 66. http://dx.doi.org/10.1021/ed067p66.

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Carlos Alberto Tosta Machado, Herman Augusto Lepikson, Matheus Antônio Nogueira de Andrade, and Paulo Renato Câmera da Silva. "Essential Oil Steam Distillation: Manufacturing 4.0." JOURNAL OF BIOENGINEERING AND TECHNOLOGY APPLIED TO HEALTH 4, no. 3 (November 26, 2021): 95–99. http://dx.doi.org/10.34178/jbth.v4i3.170.

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Smart sensors, self-configuration, operational flexibility, and automatic learning, among others, are technological attributes from industry 4.0 appliable to the essential oil extraction by the steam distillation process. These operations are recognized by their simplicity. Nevertheless, lack of automatic controls, process monitoring, and self-adjustment lead to uncontrolled extraction, poor yields, low quality of products. It occurs because of overexposure to high temperatures and overspending resources like energy and water. As far as capacity utilization is concerned, the optimized process is key to planning and managing the production activities.

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Foucher, Etienne R., Michael F. Doherty, and Michael F. Malone. "Automatic screening of entrainers for homogeneous azeotropic distillation." Industrial & Engineering Chemistry Research 30, no. 4 (April 1991): 760–72. http://dx.doi.org/10.1021/ie00052a021.

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Diaz, Simon, Jose Ricardo Perez-Correa, and Mario Alberto Fernandez-Fernandez. "Automatic Control on Batch and Continuous Distillation Columns." IEEE Latin America Transactions 16, no. 9 (September 2018): 2418–26. http://dx.doi.org/10.1109/tla.2018.8789563.

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Wang, Shuai, and Chunwu Liu. "Automatic Modulation Classification with Neural Networks via Knowledge Distillation." Electronics 11, no. 19 (September 22, 2022): 3018. http://dx.doi.org/10.3390/electronics11193018.

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Deep learning is used for automatic modulation recognition in neural networks, and because of the need for high classification accuracy, deeper and deeper networks are used. However, these are computationally very expensive for neural network training and inference, so its utility in the case of a mobile with memory limitations or weak computational power is questionable. As a result, a trade-off between network depth and network classification accuracy must be considered. To address this issue, we used a knowledge distillation method in this study to improve the classification accuracy of a small network model. First, we trained Inception–Resnet as a teacher network, which has a size of 311.77 MB and a final peak classification accuracy of 93.09%. We used the method to train convolutional neural network 3 (CNN3) and increase its peak classification accuracy from 79.81 to 89.36%, with a network size of 0.37 MB. It was also used similarly to train mini Inception–Resnet and increase its peak accuracy from 84.18 to 93.59%, with a network size of 39.69 MB. When we compared all classification accuracy peaks, we discover that knowledge distillation improved small networks and that the student network had the potential to outperform the teacher network. Using knowledge distillation, a small network model can achieve the classification accuracy of a large network model. In practice, choosing the appropriate student network based on the constraints of the usage conditions while using knowledge distillation (KD) would be a way to meet practical needs.

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Drevetskiy, Volodymyr, and Vasyl Ivanchuk. "COMPUTER MODELLING OF AUTOMATIC CONTROL PROCESS OF DISTILLATION COLUMN." Informatics, Control, Measurement in Economy and Environment Protection 6, no. 3 (August 1, 2016): 47–50. http://dx.doi.org/10.5604/20830157.1212267.

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Ansari, Shahzeb. "PLC based Automatic Distillation and Collection of Ethanol-Water Solution." Indian Journal of Science and Technology 9, no. 1 (January 20, 2016): 1–6. http://dx.doi.org/10.17485/ijst/2016/v9i47/108655.

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Farrukh, Muhammad, Irfan Ahmed Halepoto, Bhawani Shankar Chowdhry, Hameedullah Kazi, and Bharat Lal. "Design and Implementation of PLC based Automatic Liquid Distillation System." Indian Journal of Science and Technology 10, no. 29 (February 1, 2017): 1–6. http://dx.doi.org/10.17485/ijst/2017/v10i29/117368.

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Li, Zerui, Yue Ming, Lei Yang, and Jing-Hao Xue. "Mutual-learning sequence-level knowledge distillation for automatic speech recognition." Neurocomputing 428 (March 2021): 259–67. http://dx.doi.org/10.1016/j.neucom.2020.11.025.

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Boozarjomehry, Ramin Bozorgmehry, Ali Pourahmadi Laleh, and William Y. Svrcek. "Automatic design of conventional distillation column sequence by genetic algorithm." Canadian Journal of Chemical Engineering 87, no. 3 (June 2009): 477–92. http://dx.doi.org/10.1002/cjce.20175.

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Firmansyah, Deni, Ibrahim Lammada, and Gina Lova Sari. "IMPLEMENTATION OF AUTOMATIC PUMP CONTROL ON SEA WATER DESTILATION SYSTEM." Electro Luceat 6, no. 2 (November 3, 2020): 299–307. http://dx.doi.org/10.32531/jelekn.v6i2.268.

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This study aims to design an automatic pump control system and assess the effectiveness of using pumps in seawater distillation systems. The system uses an Arduino Uno microcontroller for central control of all system activities and a relay as a pump switch. Ultrasonic sensors, pH sensors and TDS sensors as input to provide information on the condition of the water in the storage container, the water pump as an output for pumping and flowing sea water and distilled water. The water pump in the seawater distillation system is used to drain seawater to the initial reservoir with a water level set point >20cm, if the set point is met, the pump will turn on. The second mini pump is used to flow the distilled water to the final reservoir with a set point value of pH <8 and a TDS value <1400, so the pump will turn on. The third mini pump is used to flow distilled water to the second basin with a set point value of pH >8 and a value of TDS >1400, so the pump will turn on, therefore the use of a pump in an automatic seawater distillation system is needed and its use can be said to be effective. Keywords: Water Pump, Relay, Control

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Noda, Masaru, Akifumi Kato, Shinji Hasebe, and Iori Hashimoto. "Optimal structure of batch distillation column." Computers & Chemical Engineering 23 (June 1999): S105—S108. http://dx.doi.org/10.1016/s0098-1354(99)80027-1.

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Skogestad, S., E. W. Jacobsen, and P. Lundström. "Selecting the Best Distillation Control Structure." IFAC Proceedings Volumes 22, no. 8 (August 1989): 315–22. http://dx.doi.org/10.1016/s1474-6670(17)53373-x.

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Pisitsungkakarn, Sumol Sae-Heng, and Pichitpon Neamyou. "Efficiency of Semi-Automatic Control Ethanol Distillation Using a Vacuum-Tube Parabolic Solar Collector." Energies 15, no. 13 (June 26, 2022): 4688. http://dx.doi.org/10.3390/en15134688.

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Thailand is an agricultural country with several agro-industrial by-products that can be processed into fuels. Although producing ethanol from agro-industrial by-products is an interesting option, the process of distilling ethanol from fermented agricultural products requires a high temperature to increase the ethanol concentration from 10% to 95%. In this research, solar ethanol distillation equipment incorporating a solar parabolic collector with a vacuum heat absorber tube to increase efficiency by reducing heat loss was designed and developed. An electronic device was used to control the distillation process, maintain the required temperature, and make suitable adjustments to the solar radiation acceptance angles of the parabolic solar collector. Ethanol dilution at concentrations of 10%, 15%, and 20%, and Sato (Thai Rice Wine) were used as the reactant in the distillation process. The result of distilling ethanol distillation with a semi-automatic control using a vacuum-tube parabolic solar collector showed that the thermal efficiency of the receiver was 12.61%, 13.93%, 18.58%, and 17.40%, respectively. The thermal efficiency of the heat exchanger was 11.27%, 10.76%, 13.35%, and 12.35%, respectively. The final concentration of ethanol was 67%, 76%, 82%, and 80%, respectively, and the amount of the distilled ethanol was 330 mL, 352 mL, 398 mL, and 360 mL, respectively.

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31

Chao, Philip H., Jeffery Collins, Joseph P. Argus, Wei-Yu Tseng, Jason T. Lee, and R. Michael van Dam. "Automatic concentration and reformulation of PET tracers via microfluidic membrane distillation." Lab on a Chip 17, no. 10 (2017): 1802–16. http://dx.doi.org/10.1039/c6lc01569g.

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32

Pavlenko, A. N., V. E. Zhukov, N. I. Pecherkin, A. D. Nazarov, A. F. Serov, N. B. Mis’kiv, X. Li, et al. "Automated control system for the fluid distributor of a distillation column with a structured packing." Optoelectronics, Instrumentation and Data Processing 53, no. 1 (January 2017): 15–20. http://dx.doi.org/10.3103/s8756699017010034.

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33

Petersen, DR, RE Link, and RG Montemayor. "Precision and Relative Bias of Automatic and Manual D 1078 Distillation." Journal of Testing and Evaluation 30, no. 2 (2002): 165. http://dx.doi.org/10.1520/jte12303j.

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34

Pulis, Anna, Carlos Fernandez, Roberto Baratti, and Jesus Alvarez. "Adjustable-structure design for ternary distillation columns." IFAC Proceedings Volumes 40, no. 5 (2007): 309–14. http://dx.doi.org/10.3182/20070606-3-mx-2915.00050.

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35

Kuncoro, Adhiguna, Lingpeng Kong, Daniel Fried, Dani Yogatama, Laura Rimell, Chris Dyer, and Phil Blunsom. "Syntactic Structure Distillation Pretraining for Bidirectional Encoders." Transactions of the Association for Computational Linguistics 8 (December 2020): 776–94. http://dx.doi.org/10.1162/tacl_a_00345.

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Textual representation learners trained on large amounts of data have achieved notable success on downstream tasks; intriguingly, they have also performed well on challenging tests of syntactic competence. Hence, it remains an open question whether scalable learners like BERT can become fully proficient in the syntax of natural language by virtue of data scale alone, or whether they still benefit from more explicit syntactic biases. To answer this question, we introduce a knowledge distillation strategy for injecting syntactic biases into BERT pretraining, by distilling the syntactically informative predictions of a hierarchical—albeit harder to scale—syntactic language model. Since BERT models masked words in bidirectional context, we propose to distill the approximate marginal distribution over words in context from the syntactic LM. Our approach reduces relative error by 2–21% on a diverse set of structured prediction tasks, although we obtain mixed results on the GLUE benchmark. Our findings demonstrate the benefits of syntactic biases, even for representation learners that exploit large amounts of data, and contribute to a better understanding of where syntactic biases are helpful in benchmarks of natural language understanding.

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36

Giessler, S., R. Y. Danilov, R. Y. Pisarenko, L. A. Serafimov, S. Hasebe, and I. Hashimoto. "Systematic structure generation for reactive distillation processes." Computers & Chemical Engineering 25, no. 1 (January 2001): 49–60. http://dx.doi.org/10.1016/s0098-1354(00)00632-3.

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37

Yang, Dae R., Dale E. Seborg, and Duncan A. Mellichamp. "Combined balance control structure for distillation columns." Industrial & Engineering Chemistry Research 30, no. 9 (September 1991): 2159–68. http://dx.doi.org/10.1021/ie00057a016.

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38

Hibara, Akihide, Kunihiko Toshin, Takehiko Tsukahara, Kazuma Mawatari, and Takehiko Kitamori. "Microfluidic Distillation Utilizing Micro–Nano Combined Structure." Chemistry Letters 37, no. 10 (October 5, 2008): 1064–65. http://dx.doi.org/10.1246/cl.2008.1064.

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39

Krolikowski, Lechoslaw J. "Feasible separation regions for distillation I: Structure." AIChE Journal 63, no. 11 (June 29, 2017): 4847–61. http://dx.doi.org/10.1002/aic.15839.

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40

Wang, Xi, Zengzhi Du, Yunlu Zhang, Jingde Wang, Jianhong Wang, and Wei Sun. "Optimization of Distillation Sequences with Nonsharp Separation Columns." Processes 7, no. 6 (May 31, 2019): 323. http://dx.doi.org/10.3390/pr7060323.

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Nonsharp distillation sequences are widely used in industrial separation processes; however, most current research has not discussed this topic, except in sequences with heat integration under special operating conditions, including complex columns. The sequence with nonsharp separation has the features of general distillation sequences, which are usually optimized by adjusting the separation sequence and the design/operation parameters of each column in the sequence, making the optimization a mixed integer nonlinear programming (MINLP) problem, which is usually hard to solve. With inclusion of nonsharp separation columns, the sequence optimization becomes even more complicated and computationally intensive. This work aimed to optimize the distillation sequence, including nonsharp distillation alongside simple columns and dividing wall columns. Inspired by the dynamic programing method for sharp distillation sequence, a framework for automatic optimization is proposed to decompose the MINLP problem into integer programming (IP) and nonlinear programming (NLP) problems. The optimization processes of sharp and nonsharp distillation sequences are compared and the solution space in terms of the possible number of distillation sequences with nonsharp separation is discussed. Two optimization cases, including an industrial one, are included to validate the proposed framework.

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41

Paluch, Justyna, Joanna Kozak, Karolina Mermer, Iwona Molęda, Marcin Wieczorek, Sławomir Kalinowski, and Paweł Kościelniak. "Novel Integrated Flow-Based Steam Distillation and Titration System for Determination of Volatile Acidity in Wines." Molecules 26, no. 24 (December 18, 2021): 7673. http://dx.doi.org/10.3390/molecules26247673.

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Novel integrated flow-based steam distillation and titration system with spectrophotometric detection was developed for determination of volatile acidity in wines. Using the system, the distillation procedure was carried out in an automatic manner, starting with introducing into a heated steam distillation module a sample and subjecting it to steam distillation. Under selected conditions, all the analyte was transferred to the distillate; therefore, the system did not require calibration. The collected distillate and titrant were introduced into the next monosegments in varying proportions, in accordance with the developed titration procedure, and directed to the detection system to record the titration curve. The titration was stopped after reaching the end point of titration. Procedures for distillation and titration were developed and verified separately by distillation of acetic acid, acetic acid in the presence of tartaric acid as well as acetic acid, tartaric acid, and titratable acidity, with precision (relative standard deviation) and accuracy (relative error) for both procedures lower than 6.9 and 5.6%, respectively. The developed steam distillation and titration systems were used to determine volatile acidity in samples of white and rosé wines separately and as the integrated steam distillation and titration system, both with precision lower than 9.4% and accuracy better than 6.7%.

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42

Bik, Aart J. C., and Harry A. G. Wijshoff. "Automatic Nonzero Structure Analysis." SIAM Journal on Computing 28, no. 5 (January 1999): 1576–87. http://dx.doi.org/10.1137/s009753979529595x.

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43

Sheikus, Anton. "DEVELOPMENT OF A SYSTEM OF AUTOMATIC MOBILE CONTROL OF THE DISTILLATION PROCESS." Innovative Technologies and Scientific Solutions for Industries, no. 4 (14) (December 21, 2020): 94–103. http://dx.doi.org/10.30837/itssi.2020.14.094.

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44

Sheikus, Anton. "RESEARCH OF TRANSIENT RESPONSES IN AUTOMATIC MOBILE CONTROL SYSTEMS OF DISTILLATION PROCESS." Technology Transfer: fundamental principles and innovative technical solutions 3 (November 23, 2019): 20–22. http://dx.doi.org/10.21303/2585-6847.2019.001043.

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45

Dadhe, K., S. Engell, R. Gesthuisen, S. Pegel, and M. Völker. "CONTROL STRUCTURE SELECTION FOR A REACTIVE DISTILLATION COLUMN." IFAC Proceedings Volumes 35, no. 1 (2002): 423–28. http://dx.doi.org/10.3182/20020721-6-es-1901.01624.

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46

Viel, F., E. Busvelle, and J. P. Gauthier. "A stable control structure for binary distillation columns." International Journal of Control 67, no. 4 (January 1997): 475–506. http://dx.doi.org/10.1080/002071797224036.

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47

Hansen, Jens Erik, Sten Bay Jørgensen, Jonathan Heath, and John D. Perkins. "Control structure selection for energy integrated distillation column." Journal of Process Control 8, no. 3 (January 1998): 185–95. http://dx.doi.org/10.1016/s0959-1524(97)00039-5.

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48

Hasebe, Shinji, Taketoshi Kurooka, Kei Higashimae, and Iori Hashimoto. "Selection of Optimal Structure of Batch Distillation System." KAGAKU KOGAKU RONBUNSHU 22, no. 6 (1996): 1351–58. http://dx.doi.org/10.1252/kakoronbunshu.22.1351.

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49

Osuolale, Funmilayo, and Jie Zhang. "Distillation Control Structure Selection for Energy-Efficient Operations." Chemical Engineering & Technology 38, no. 5 (April 8, 2015): 907–16. http://dx.doi.org/10.1002/ceat.201400707.

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50

Rooks, Raymond E., Vivek Julka, Michael F. Doherty, and Michael F. Malone. "Structure of Distillation Regions for Multicomponent Azeotropic Mixtures." AIChE Journal 44, no. 6 (June 1998): 1382–91. http://dx.doi.org/10.1002/aic.690440616.

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