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Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

Alekseev, К. А., S. M. Kirichenko, А. V. Rakov, R. А. Gaifutdinov, М. I. Farakhov, А. G. Laptev, А. N. Volkov, I. Е. Sennikov, N. V. Ledneva, and А. А. Shchepalov. "An Approach to Stabilize the Composition of Heavy Vacuum Gas Oil in the Production of Lubricating Oils." Chemistry and Technology of Fuels and Oils 630, no. 2 (2022): 8–13. http://dx.doi.org/10.32935/0023-1169-2022-630-2-8-13.

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A heavy vacuum gas oil separation unit was developed and implemented at an industrial enterprise with a stabilized composition. The technical solutions, the selected equipment and the results of the installation operation are described. On a laboratory model of a distillation column, experiments were made to develop technological conditions. In calculations of an industrial column with a new regular packing applied previously developed mathematical model of multicomponent distillaton. The mixture is represented as pseudo-binary in terms of fractions. In addition, software packages for plate-by-plate calculation of the column. The selection of the main and auxiliary equipment of the industrial vacuum distillation plant was carried out.

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2

Opatrný, T., and G. Kurizki. "Optimization approach to entanglement distillation." Physical Review A 60, no. 1 (July 1, 1999): 167–72. http://dx.doi.org/10.1103/physreva.60.167.

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3

LI, Lianqiang, Kangbo SUN, and Jie ZHU. "A Novel Multi-Knowledge Distillation Approach." IEICE Transactions on Information and Systems E104.D, no. 1 (January 1, 2021): 216–19. http://dx.doi.org/10.1587/transinf.2020edl8080.

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4

Prodanovic, Sasa, Novak Nedic, Vojislav Filipovic, and Ljubisa Dubonjic. "Modified approach to distillation column control." Chemical Industry 71, no. 3 (2017): 183–93. http://dx.doi.org/10.2298/hemind160326028p.

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Анотація:

This paper contains methodology research for forming the control algorithm for a distillation column, modeled as TITO (two-input two-output) process. Its modified form was obtained by connecting the two parts, and this combination hasn't been applied for such a industrial plant, until now. These parts include: a simplified decoupler which was first designed and decentralized PID controller obtained using D-decomposition method for such decoupled process. The decoupler was designed in order to make process become diagonal, and parameters of PID controllers are defined for the two SISO (single-input single-output) processes starting from relation between IE (integral error) criterion and integrator gain, taking into account desired response characteristics deriving from technological requirements of controlled plant. Their connecting provides centralized control. Analysis of the processes responses, obtained by the proposed algorithm and their comparison with the results from the literature, were performed after the completion of the simulations. The proposed approach to the centralized controller design, beside its simplicity of usage and flexibility in achieving diversity of process dynamic behavior, gives better response characteristics, in comparison with existing control algorithms for distillation column in the literature.

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5

Phiciato, S. Rianda, C. Irawan, and D. Sinaga. "Characterization approach to develop distillation process for production of anode-grade coal tar pitch." IOP Conference Series: Earth and Environmental Science 882, no. 1 (November 1, 2021): 012035. http://dx.doi.org/10.1088/1755-1315/882/1/012035.

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Abstract Many efforts have been spent to make a trade-off between designing an efficient distillation system and meeting very strict requirements of pitch product. The design of coal tar distillation for pitch production should be able not only energetically efficient but also to meet the physicochemical requirements of pitch. This paper presents a practical approach and a systematic method of material characterizations to evaluate appropriate distillation operating conditions. The purpose of this study was to develop a simple coal tar distillation process that focused on obtaining anode-grade binder having certain specifications. The distillation process was carried out in two stages. The first stage occurred up to 360-370°C at atmospheric pressure to separate all volatile fractions. The second stage distillation involves vacuum pressure with 4 conditions in which A<B<C<D ranging from -4 to -35 cmHg and varying soaking time to convert soft pitch into hard pitch. Higher vacuum pressure of distillation is not necessarily high to meet specifications. Our finding shows that B is the most favourable vacuum pressure and can be further heat treated for specific requirements of pitch.

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6

Binh, Le Minh, and Simon Woo. "ADD: Frequency Attention and Multi-View Based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 1 (June 28, 2022): 122–30. http://dx.doi.org/10.1609/aaai.v36i1.19886.

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Анотація:

Despite significant advancements of deep learning-based forgery detectors for distinguishing manipulated deepfake images, most detection approaches suffer from moderate to significant performance degradation with low-quality compressed deepfake images. Because of the limited information in low-quality images, detecting low-quality deepfake remains an important challenge. In this work, we apply frequency domain learning and optimal transport theory in knowledge distillation (KD) to specifically improve the detection of low-quality compressed deepfake images. We explore transfer learning capability in KD to enable a student network to learn discriminative features from low-quality images effectively. In particular, we propose the Attention-based Deepfake detection Distiller (ADD), which consists of two novel distillations: 1) frequency attention distillation that effectively retrieves the removed high-frequency components in the student network, and 2) multi-view attention distillation that creates multiple attention vectors by slicing the teacher’s and student’s tensors under different views to transfer the teacher tensor’s distribution to the student more efficiently. Our extensive experimental results demonstrate that our approach outperforms state-of-the-art baselines in detecting low-quality compressed deepfake images.

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7

Kanthasamy, Ramesh, Hisyam Anwaruddin, and Suriya Kumar Sinnadurai. "A New Approach to the Identification of Distillation Column Based on Hammerstein Model." Modelling and Simulation in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/813757.

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Developing a suitable nonlinear model is the most challenging problem in the application of nonlinear model based controllers to distillation column. Hammerstein model consists of a nonlinear static element described by wavenet based nonlinear function, followed by a linear dynamic element described by the Output Error(OE) model was used in this study to represent the nonlinear dynamics of the distillation column. The model parameters were identified using iterative prediction-error minimization method. The model validation results proved that the Hammerstein model was capable of capturing the nonlinear dynamics of distillation column.

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8

Swartz, C. L. E., and W. E. Stewart. "A collocation approach to distillation column design." AIChE Journal 32, no. 11 (November 1986): 1832–38. http://dx.doi.org/10.1002/aic.690321108.

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9

Bildea, Costin S., and Alexandre C. Dimian. "Singularity theory approach to ideal binary distillation." AIChE Journal 45, no. 12 (December 1999): 2662–66. http://dx.doi.org/10.1002/aic.690451224.

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10

Lory, P. "Short-Cut Distillation Columns: A Mathematical Approach." ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik 78, S3 (1998): 999–1000. http://dx.doi.org/10.1002/zamm.19980781569.

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11

Del Pozo Gomez, M. T., Andreas Klein, Jens-Uwe Repke, and Günter Wozny. "A new energy-integrated pervaporation distillation approach." Desalination 224, no. 1-3 (April 2008): 28–33. http://dx.doi.org/10.1016/j.desal.2007.04.075.

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12

Mendez-B, Juancarlos, Gilberto Gonzalez-Avalos, Noe Barrera Gallegos, Gerardo Ayala-Jaimes, and Carlos Rubio-Maya. "Modeling and Simulation of an Energy Integrated Distillation Column in a Bond Graph Approach." Entropy 24, no. 9 (August 25, 2022): 1191. http://dx.doi.org/10.3390/e24091191.

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Анотація:

The bond graph methodology for modelling an integrated energy distillation column is applied in this paper. The distillation column is built by five trays for a binary mixture. However, due to its modular construction in a bond graph, the number of trays can be increased. In order to link the analysis tools of systems modeled in the bond graph to the mathematical model given to a distillation column, a junction structure of the proposed bond graph is presented. Hence, this junction structure is a way to obtain the state space representation of the modeled column in bond graphs. Likewise, it is well known that distillation columns determine a class of nonlinear systems, so throughout this paper, these systems in a bond graph approach can be analyzed. In order to learn the behavior of the distillation column in the physical domain, simulation results using 20-Sim software are shown. In addition, with the simulation of two case studies consisting of two mixtures with different relative volatilities, the versatility of the column model in a bond graph is presented. In both cases, the increase in the feed flow, the mole fraction of the light component in the feed or the distillate reflux that enriches the concentration of light in the column determine an increase in the mole fraction of light in the distillate and in the bottom reflow. Further, the control design for a distillation column in the physical domain can be extended.

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13

Šulgan, Branislav, Juraj Labovský, and Zuzana Labovská. "Multi-Aspect Comparison of Ethyl Acetate Production Pathways: Reactive Distillation Process Integration and Intensification via Mechanical and Chemical Approach." Processes 8, no. 12 (December 8, 2020): 1618. http://dx.doi.org/10.3390/pr8121618.

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This paper provides a multi-aspect comparison of selected methods of ethyl acetate production and shows the possibility of further reactive distillation process integration and sophisticated intensification including process stream regeneration. The production pathways were selected with respect to their practical applicability and sufficient experimental and feasibility studies already published. A total of four case studies were designed and compared: conventional process set-up (ethyl acetate is produced in a chemical reactor) is designed as a base case study; reactive distillation with a separation unit is derived from the conventional process set-up. The mechanical and chemical approach to reactive distillation process intensification and integration were assumed: reactive distillation column with a stripper and reactive distillation column with an auxiliary chemical reaction (ethylene oxide hydration). Process models were compiled in the Aspen Plus software. Complex process flowsheets of selected case studies including separation and regeneration were designed and optimized. Three different points of view were applied to evaluate the selected process benefits and drawbacks. Process energy, economy, and safety were assessed. As a result, a reactive distillation column with an auxiliary chemical reaction has been proven to be the most suitable pathway for ethyl acetate production assuming all three evaluated aspects.

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14

Li, Yiwei, Shaoxiong Feng, Bin Sun, and Kan Li. "Heterogeneous-Branch Collaborative Learning for Dialogue Generation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (June 26, 2023): 13148–56. http://dx.doi.org/10.1609/aaai.v37i11.26544.

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With the development of deep learning, advanced dialogue generation methods usually require a greater amount of computational resources. One promising approach to obtaining a high-performance and lightweight model is knowledge distillation, which relies heavily on the pre-trained powerful teacher. Collaborative learning, also known as online knowledge distillation, is an effective way to conduct one-stage group distillation in the absence of a well-trained large teacher model. However, previous work has a severe branch homogeneity problem due to the same training objective and the independent identical training sets. To alleviate this problem, we consider the dialogue attributes in the training of network branches. Each branch learns the attribute-related features based on the selected subset. Furthermore, we propose a dual group-based knowledge distillation method, consisting of positive distillation and negative distillation, to further diversify the features of different branches in a steadily and interpretable way. The proposed approach significantly improves branch heterogeneity and outperforms state-of-the-art collaborative learning methods on two widely used open-domain dialogue datasets.

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15

Chaudhury, Sushovan, Nilesh Shelke, Kartik Sau, B. Prasanalakshmi, and Mohammad Shabaz. "A Novel Approach to Classifying Breast Cancer Histopathology Biopsy Images Using Bilateral Knowledge Distillation and Label Smoothing Regularization." Computational and Mathematical Methods in Medicine 2021 (October 20, 2021): 1–11. http://dx.doi.org/10.1155/2021/4019358.

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Breast cancer is the most common invasive cancer in women and the second main cause of cancer death in females, which can be classified benign or malignant. Research and prevention on breast cancer have attracted more concern of researchers in recent years. On the other hand, the development of data mining methods provides an effective way to extract more useful information from complex databases, and some prediction, classification, and clustering can be made according to the extracted information. The generic notion of knowledge distillation is that a network of higher capacity acts as a teacher and a network of lower capacity acts as a student. There are different pipelines of knowledge distillation known. However, previous work on knowledge distillation using label smoothing regularization produces experiments and results that break this general notion and prove that knowledge distillation also works when a student model distils a teacher model, i.e., reverse knowledge distillation. Not only this, but it is also proved that a poorly trained teacher model trains a student model to reach equivalent results. Building on the ideas from those works, we propose a novel bilateral knowledge distillation regime that enables multiple interactions between teacher and student models, i.e., teaching and distilling each other, eventually improving each other’s performance and evaluating our results on BACH histopathology image dataset on breast cancer. The pretrained ResNeXt29 and MobileNetV2 models which are already tested on ImageNet dataset are used for “transfer learning” in our dataset, and we obtain a final accuracy of more than 96% using this novel approach of bilateral KD.

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16

Obumneme Onyeka Okwonna and Amalate Ann Jonathan Obuebite. "Artificial intelligence approach in crude distillation unit operation." Global Journal of Engineering and Technology Advances 9, no. 2 (November 30, 2021): 075–82. http://dx.doi.org/10.30574/gjeta.2021.9.2.0155.

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This study incorporates the use of Artificial Intelligence in the monitoring of atmospheric distillation unit of large scale refining operation using Google AutoML tables, Jupyter, and Python software. The process involved training, evaluation, improvement, and deployment of the models based on the input data. The predicted yield (vol %) for the models were: Auto ML model: liquefied petroleum gas (LPG) - 1.41 , straight run gasoline (SRG)– 4.96, straight run naphtha (SRN) – 17.87, straight run kerosene (SRK) – 14.5, light diesel oil (LDO) – 26.47, heavy diesel oil (HDO) – 2.7, and atmospheric residue (AR) –30.03; Jupyter Model: LPG – (0.93), SRG – (4.69), SRN – (17.24), SRK – (14.39), LDO – (26.43), HDO – (2.7), and AR – (30.18); and Python Model:LPG – (1.66) , SRG – (7.58), SRN – (11.68), SRK – (14.92), LDO – (24.77), HDO – (4.59), and AR – (24.59). The coefficient of determination (R2) values of 0.99981, 0.99943, and 0.93078 and Standard Error values of 0.240918, 0.419291, 3.536064, were obtained for the 3 models, respectively. All the software gave good predictions of the actual yield, although the Google Auto ML Table gave the best prediction. The training of the model is fundamental to its performance and precision.

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17

Zumoffen, D., G. Molina, L. Nieto, and M. Basualdo. "Systematic Control Approach for the Petlyuk Distillation Column*." IFAC Proceedings Volumes 44, no. 1 (January 2011): 8552–57. http://dx.doi.org/10.3182/20110828-6-it-1002.02220.

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18

Khalid, Syed Safwan, Muhammad Awais, Zhen-Hua Feng, Chi-Ho Chan, Ammarah Farooq, Ali Akbari, and Josef Kittler. "Resolution Invariant Face Recognition Using a Distillation Approach." IEEE Transactions on Biometrics, Behavior, and Identity Science 2, no. 4 (October 2020): 410–20. http://dx.doi.org/10.1109/tbiom.2020.3007356.

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19

Hauan, S., and K. M. Lien. "A Phenomena Based Design Approach to Reactive Distillation." Chemical Engineering Research and Design 76, no. 3 (March 1998): 396–407. http://dx.doi.org/10.1205/026387698524820.

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20

Lüder, T., and G. Wozny. "An Analytical Approach to Modelling in Distillation Control." IFAC Proceedings Volumes 25, no. 5 (April 1992): 185–92. http://dx.doi.org/10.1016/s1474-6670(17)50990-8.

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21

Lima, Nádson Murilo Nascimento, Lamia Zuñiga Liñan, Flavio Manenti, Rubens Maciel Filho, Maria Regina Wolf Maciel, Marcelo Embiruçu, and Lílian Carmen Medina. "Fuzzy cognitive approach of a molecular distillation process." Chemical Engineering Research and Design 89, no. 4 (April 2011): 471–79. http://dx.doi.org/10.1016/j.cherd.2010.08.010.

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22

Tabari, Amir, and Arshad Ahmad. "A semicontinuous approach for heterogeneous azeotropic distillation processes." Computers & Chemical Engineering 73 (February 2015): 183–90. http://dx.doi.org/10.1016/j.compchemeng.2014.12.005.

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23

Maiti, S. N., S. Ganguly, A. K. Das, and D. N. Saraf. "Multicomponent Distillation Column Design: A Semi-rigorous Approach." Developments in Chemical Engineering and Mineral Processing 2, no. 1 (May 15, 2008): 37–52. http://dx.doi.org/10.1002/apj.5500020105.

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24

Sun, Suli, Zhe Cui, Xiang Zhang, and Wende Tian. "A Hybrid Inverse Problem Approach to Model-Based Fault Diagnosis of a Distillation Column." Processes 8, no. 1 (January 2, 2020): 55. http://dx.doi.org/10.3390/pr8010055.

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Анотація:

Early-stage fault detection and diagnosis of distillation has been considered an essential technique in the chemical industry. In this paper, fault diagnosis of a distillation column is formulated as an inverse problem. The nonlinear least squares algorithm is used to evaluate fault parameters embedded in a nonlinear dynamic model of distillation once abnormal symptoms are detected. A partial least squares regression model is built based on fault parameter history to explicitly predict the development of fault parameters. With the stripper of Tennessee Eastman process as example, this novel approach is tested for step- and random-type faults and several factors affecting its efficiency are discussed. The application result shows that the hybrid inverse problem approach gives the correct change of fault parameter at a speed far faster than the base approach with only a nonlinear model.

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25

Liu, Tengjun, Ying Chen, and Wanxuan Gu. "Copyright-Certified Distillation Dataset: Distilling One Million Coins into One Bitcoin with Your Private Key." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 5 (June 26, 2023): 6458–66. http://dx.doi.org/10.1609/aaai.v37i5.25794.

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The rapid development of neural network dataset distillation in recent years has provided new ideas in many areas such as continuous learning, neural network architecture search and privacy preservation. Dataset distillation is a very effective method to distill large training datasets into small data, thus ensuring that the test accuracy of models trained on their synthesized small datasets matches that of models trained on the full dataset. Thus, dataset distillation itself is commercially valuable, not only for reducing training costs, but also for compressing storage costs and significantly reducing the training costs of deep learning. However, copyright protection for dataset distillation has not been proposed yet, so we propose the first method to protect intellectual property by embedding watermarks in the dataset distillation process. Our approach not only popularizes the dataset distillation technique, but also authenticates the ownership of the distilled dataset by the models trained on that distilled dataset.

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26

Nhien, Le Cao, Neha Agarwal, and Moonyong Lee. "Dehydration of Isopropanol: A Comparative Review of Distillation Processes, Heat Integration, and Intensification Techniques." Energies 16, no. 16 (August 11, 2023): 5934. http://dx.doi.org/10.3390/en16165934.

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The dehydration of isopropanol (IPA) is a crucial process in numerous industries, and the optimization of its efficiency and economic viability is essential. This review provides a comprehensive analysis and comparison of various distillation processes, heat integration (HI) strategies, and process intensification (PI) techniques employed for IPA dehydration. The advantages, limitations, and applicability of distillation processes, such as extractive distillation, heterogeneous azeotropic distillation, and pressure swing distillation, are discussed. In addition, this review explores the potential of HI techniques to optimize energy consumption and reduce operating costs of IPA dehydration processes. PI techniques, including thermally coupled arrangements and dividing wall columns, are examined for their ability to improve the process efficiency and sustainability. It is crucial to conduct thorough evaluations, as well as energy and economic analyses, when choosing the appropriate distillation process, HI approach, and PI technique for specific IPA dehydration applications. This review emphasizes the potential for improving the energy efficiency, product purity, and cost-effectiveness of IPA dehydration through the integration of advanced distillation processes and PI techniques.

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27

Ganesan, Timothy, and Irraivan Elamvazuthi. "Entanglement Distillation Optimization Using Fuzzy Relations for Quantum State Tomography." Algorithms 16, no. 7 (June 25, 2023): 313. http://dx.doi.org/10.3390/a16070313.

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Practical entanglement distillation is a critical component in quantum information theory. Entanglement distillation is often utilized for designing quantum computer networks and quantum repeaters. The practical entanglement distillation problem is formulated as a bilevel optimization problem. A fuzzy formulation is introduced to estimate the quantum state (density matrix) from pseudo-likelihood functions (i.e., quantum state tomography). A scale-independent relationship between fuzzy relations in terms of the pseudo-likelihood functions is obtained. The entanglement distillation optimization problem is solved using the combined coupled map lattice and dual annealing approach. Comparative analysis of the results is then conducted against a standard dual annealing algorithmic implementation.

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28

Smets, I. Y., S. Boon, T. Boelen, J. Espinosa, and J. F. Van Impe. "INFERRING DISTILLATION PRODUCT COMPOSITION: A HYBRID SOFT SENSOR APPROACH." IFAC Proceedings Volumes 40, no. 5 (2007): 167–72. http://dx.doi.org/10.3182/20070606-3-mx-2915.00146.

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29

Rehm, Ansgar. "A General Quadratic Performance Approach to Binary Distillation Control." IFAC Proceedings Volumes 42, no. 11 (2009): 441–46. http://dx.doi.org/10.3182/20090712-4-tr-2008.00070.

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30

Nghiem, Long D., and Tzahi Cath. "A scaling mitigation approach during direct contact membrane distillation." Separation and Purification Technology 80, no. 2 (July 2011): 315–22. http://dx.doi.org/10.1016/j.seppur.2011.05.013.

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31

Schenk, M., R. Gani, D. Bogle, and E. N. Pistikopoulos. "A Hybrid Modelling Approach for Separation Systems Involving Distillation." Chemical Engineering Research and Design 77, no. 6 (September 1999): 519–34. http://dx.doi.org/10.1205/026387699526557.

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32

Bruno, Thomas J., Lisa S. Ott, Beverly L. Smith, and Tara M. Lovestead. "Complex Fluid Analysis with the Advanced Distillation Curve Approach." Analytical Chemistry 82, no. 3 (February 2010): 777–83. http://dx.doi.org/10.1021/ac902002j.

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33

H., M., and Heba A. "Fuzzy Analogical Gates Approach for Heat Integrated Distillation Systems." International Journal of Computer Applications 133, no. 2 (January 15, 2016): 24–32. http://dx.doi.org/10.5120/ijca2016907541.

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34

Greaves, M. A., I. M. Mujtaba, M. Barolo, A. Trotta, and M. A. Hussain. "Neural-Network Approach to Dynamic Optimization of Batch Distillation." Chemical Engineering Research and Design 81, no. 3 (March 2003): 393–401. http://dx.doi.org/10.1205/02638760360596946.

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35

Friberg, Stig E., and Patricia A. Aikens. "Fragrance Emulsion Evaporation versus Distillation: A Phase Diagram Approach." Journal of Dispersion Science and Technology 31, no. 5 (April 21, 2010): 627–31. http://dx.doi.org/10.1080/01932690903217957.

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36

Singh, Vijander, Indra Gupta, and H. O. Gupta. "ANN-based estimator for distillation using Levenberg–Marquardt approach." Engineering Applications of Artificial Intelligence 20, no. 2 (March 2007): 249–59. http://dx.doi.org/10.1016/j.engappai.2006.06.017.

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37

Kattan, M. K., and P. L. Douglas. "A new approach to thermal integration of distillation sequences." Canadian Journal of Chemical Engineering 64, no. 1 (February 1986): 162–70. http://dx.doi.org/10.1002/cjce.5450640124.

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38

C, Deepa, Amba Shetty, and Narasimhadhan A V. "Knowledge distillation: A novel approach for deep feature selection." Egyptian Journal of Remote Sensing and Space Science 26, no. 1 (February 2023): 63–73. http://dx.doi.org/10.1016/j.ejrs.2022.12.006.

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39

Muthia, Rahma. "Structured Data Management for Investigating an Optimum Reactive Distillation Design." ADI Journal on Recent Innovation (AJRI) 5, no. 1 (March 14, 2023): 34–42. http://dx.doi.org/10.34306/ajri.v5i1.899.

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Анотація:

Reaction and separation are the two most important processes in the chemical industry that significantly affect the overall energy and cost requirements. Therefore, the operation of integrated reactive separation in a single equipment, such as reactive distillation, potentially increases the chemical process efficiency remarkably. To achieve that advantage, it is vital to design an optimum reactive distillation configuration in the preliminary evaluation. This paper proposes structured data management that can be used to encounter an optimum reactive distillation design. The approach simplified and reduced the data necessity from thousands to dozens by applying a systematic data sorting for the simulation results obtained from the Aspen Plus simulator. Using a case study that is highly relevant to the real chemical processes, i.e., the metathesis of 2-pentene, this study showed that the rule of thumb for determining the optimum design of conventional distillation can be adapted for reactive distillation technology.

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40

Kang, Minsoo, Jonghwan Mun, and Bohyung Han. "Towards Oracle Knowledge Distillation with Neural Architecture Search." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 4404–11. http://dx.doi.org/10.1609/aaai.v34i04.5866.

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We present a novel framework of knowledge distillation that is capable of learning powerful and efficient student models from ensemble teacher networks. Our approach addresses the inherent model capacity issue between teacher and student and aims to maximize benefit from teacher models during distillation by reducing their capacity gap. Specifically, we employ a neural architecture search technique to augment useful structures and operations, where the searched network is appropriate for knowledge distillation towards student models and free from sacrificing its performance by fixing the network capacity. We also introduce an oracle knowledge distillation loss to facilitate model search and distillation using an ensemble-based teacher model, where a student network is learned to imitate oracle performance of the teacher. We perform extensive experiments on the image classification datasets—CIFAR-100 and TinyImageNet—using various networks. We also show that searching for a new student model is effective in both accuracy and memory size and that the searched models often outperform their teacher models thanks to neural architecture search with oracle knowledge distillation.

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41

Kruber, Kai Fabian, and Mirko Skiborowski. "Topology-Based Initialization for the Optimization-Based Design of Heteroazeotropic Distillation Processes." Processes 10, no. 8 (July 28, 2022): 1482. http://dx.doi.org/10.3390/pr10081482.

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Анотація:

Distillation-based separation processes, such as extractive or heteroazeotropic distillation, present important processes for separating azeotropic mixtures in the chemical and biochemical industry. However, heteroazeotropic distillation has received much less attention than extractive distillation, which can be attributed to multiple reasons. The phase equilibrium calculations require a correct evaluation of phase stability, while the topology of the heterogeneous mixtures is generally more complex, comprising multiple azeotropes and distillation regions, resulting in an increased modeling complexity. Due to the integration of distillation columns and a decanter, even the simulation of these processes is considered more challenging, while an optimal process design should include the selection of a suitable solvent, considering the performance of the integrated hybrid process. Yet, the intricate mixture topologies largely impede the use of simplified criteria for solvent selection. To overcome these limitations and allow for a process-based screening of potential solvents, the current work presents a topology-based initialization and optimization approach for designing heteroazeotropic distillation processes. The systematic initialization enables an efficient evaluation of different solvents with different mixture topologies, which is further exploited for optimization-based sensitivity analysis and multi-objective optimization. Three case studies are analyzed with about 170 individually optimized process designs, including stage numbers, feed locations, phase ratios, and heat duties.

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42

Al-yaqoobi, Atheer, David Hogg, and William B. Zimmerman. "Microbubble Distillation for Ethanol-Water Separation." International Journal of Chemical Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/5210865.

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In the current study, a novel approach for separating ethanol-water mixture by microbubble distillation technology was investigated. Traditional distillation processes require large amounts of energy to raise the liquid to its boiling point to effect removal of volatile components. The concept of microbubble distillation by comparison is to heat the gas phase rather than the liquid phase to achieve separation. The removal of ethanol from the thermally sensitive fermentation broths was taken as a case of study. Consequently the results were then compared with those which could be obtained under equilibrium conditions expected in an “ideal” distillation unit. Microbubble distillation has achieved vapour compositions higher than that which could be obtained under traditional equilibrium conditions. The separation was achieved at liquid temperature significantly less than the boiling point of the mixture. In addition, it was observed that the separation efficiency of the microbubble distillation could be increased by raising the injected air temperature, while the temperature of the liquid mixture increased only moderately. The separation efficiency of microbubble distillation was compared with that of pervaporation for the recovery of bioethanol from the thermally sensitive fermentation broths. The technology could be controlled to give high separation and energy efficiency. This could contribute to improving commercial viability of biofuel production and other coproducts of biorefinery processing.

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43

Ji, Mingi, Byeongho Heo, and Sungrae Park. "Show, Attend and Distill: Knowledge Distillation via Attention-based Feature Matching." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 9 (May 18, 2021): 7945–52. http://dx.doi.org/10.1609/aaai.v35i9.16969.

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Knowledge distillation extracts general knowledge from a pretrained teacher network and provides guidance to a target student network. Most studies manually tie intermediate features of the teacher and student, and transfer knowledge through predefined links. However, manual selection often constructs ineffective links that limit the improvement from the distillation. There has been an attempt to address the problem, but it is still challenging to identify effective links under practical scenarios. In this paper, we introduce an effective and efficient feature distillation method utilizing all the feature levels of the teacher without manually selecting the links. Specifically, our method utilizes an attention based meta network that learns relative similarities between features, and applies identified similarities to control distillation intensities of all possible pairs. As a result, our method determines competent links more efficiently than the previous approach and provides better performance on model compression and transfer learning tasks. Further qualitative analyses and ablative studies describe how our method contributes to better distillation.

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44

Tan, Haiyan, and Lin Cong. "Modeling and Control Design for Distillation Columns Based on the Equilibrium Theory." Processes 11, no. 2 (February 16, 2023): 607. http://dx.doi.org/10.3390/pr11020607.

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Distillation columns represent the most widely used separation equipment in the petrochemical industry. It is usually difficult to apply the traditional mechanism modeling method to online optimization and control because of its complex structure, and common simplified models produce obvious errors. Therefore, we analyze the mass transfer process of gas-liquid fluid on each column tray based on the theory of gas-liquid equilibrium and establish a nonlinear dynamic model of the distillation process. The proposed model can accurately characterize the nonlinear characteristics of the distillation process, and the model structure is largely simplified compared with the traditional mechanism model. Therefore, the model provides a new approach for model-based methods in distillation columns, especially for cases that require efficient online models. Two case studies of benzene-toluene distillation systems show that the nonlinear model has high concentration observation accuracy. Finally, a generic model control scheme is designed based on this model. Simulation results show that this control strategy performs better than a traditional PID control scheme.

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45

Chen, Wen Gang, Xiao Jie Song, and Wei Liu. "Design of Automated Distillation Analyzer Based on STM32." Advanced Materials Research 383-390 (November 2011): 1215–18. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.1215.

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Aiming to solve the problems of inaccuracy in temperature control and the slow reaction in condensing system in the automated distillation analyzer, an approach which combining electrical fan and thermoelectric cooler was put forward, using STM32 as the control unit; accurate temperature control during the distillation process was realized, data collection and processing can be completed by the control unit, and the wireless module can accomplish communication and control the analyzer under the instructions of computer. With this temperature control method, the automation and test accuracy of distillation analyzer can be enhanced, thus ensuring better ratio of performance-to-price and control performance.

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46

Chen, Wen Gang, Xiao Jie Song, and Wei Liu. "Design of Automated Distillation Analyzer Based on STM32." Advanced Materials Research 433-440 (January 2012): 2607–10. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.2607.

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Анотація:

Aiming to solve the problems of inaccuracy in temperature control and the slow reaction in condensing system in the automated distillation analyzer, an approach which combining electrical fan and thermoelectric cooler was put forward, using STM32 as the control unit; accurate temperature control during the distillation process was realized, data collection and processing can be completed by the control unit, and the wireless module can accomplish communication and control the analyzer under the instructions of computer. With this temperature control method, the automation and test accuracy of distillation analyzer can be enhanced, thus ensuring better ratio of performance-to-price and control performance.

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47

Arifianto, Deni, Ampar Jaya Suwondo, M. Hasan Abdullah, Chendrasari Wahyu Octavia, Astria Hindratmo, and Onny Purnamayudhia. "Perancangan Alat Destilasi Limbah Ampas Tahu Menjadi Bahan Bakar Bioethanol Melalui Metode Quality Function Deployment (QFD)." Journal of System Engineering and Technological Innovation (JISTI) 2, no. 01 (April 27, 2023): 118–30. http://dx.doi.org/10.38156/jisti.v2i01.47.

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Анотація:

Fuel oil is experiencing an increase in demand in various sectors of life impacting the distribution process and meeting market needs. Bioethanol is a new and renewable energy with great potential to replace fuel oil. One alternative material for producing bioethanol is tofu dregs. Tofu dregs raw material processing can be done by distillation process. This study uses conventional distillation with the addition of components which are expected to produce bioethanol with good quality. The design of the distillation apparatus uses the QFD (Quality Function Development) approach. So the purpose of this study was to determine the technical characteristics of the bioethanol distillation system based on customer needs. The design of a bioethanol distillation apparatus made from Tofu Dregs using the QFD method resulted in several criteria for the device expected by the customer, including fast distillation time, amount of ethanol production, fermentation time, ergonomics, hydrolysis time, octane content and solution composition. The importance level of fast distillation time was 4.06, total ethanol production was 3.8, fermentation time was 3.67, ergonomic design was 3.63, hydrolysis time was 3.26, octane level was 3.21 and solution composition was 3.15. The resulting tool is capable of processing tofu dregs into bioethanol. The dimensions of the tool are 1264 mm in length, 1226 mm in width, and a total tool height of 1275 mm. The distillation apparatus is equipped with a set of vessels, condensers, coolers and heaters.

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48

DARBYSHIRE, PAUL. "EFFECTS OF COMMUNICATION ON GROUP LEARNING RATES IN A MULTI-AGENT ENVIRONMENT." Advances in Complex Systems 06, no. 03 (September 2003): 405–26. http://dx.doi.org/10.1142/s0219525903000979.

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Distillations utilize multi-agent based modeling and simulation techniques to study warfare as a complex adaptive system at the conceptual level. The focus is placed on the interactions between the agents to facilitate study of cause and effect between individual interactions and overall system behavior. Current distillations do not utilize machine-learning techniques to model the cognitive abilities of individual combatants but employ agent control paradigms to represent agents as highly instinctual entities. For a team of agents implementing a reinforcement-learning paradigm, the rate of learning is not sufficient for agents to adapt to this hostile environment. However, by allowing the agents to communicate their respective rewards for actions performed as the simulation progresses, the rate of learning can be increased sufficiently to significantly increase the teams chances of survival. This paper presents the results of trials to measure the success of a team-based approach to the reinforcement-learning problem in a distillation, using reward communication to increase learning rates.

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49

Dubrovskaya, Irina, Evgeniy Gerasimenko, Margarita Slobodyanik, and Sergey Sonin. "Adapting of a Method for Qualitative and Quantitative Determination of Squalene in Distillation Cuts of Sunflower Oil." BIO Web of Conferences 32 (2021): 03007. http://dx.doi.org/10.1051/bioconf/20213203007.

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Анотація:

Squalene is a naturally-occurring dihydro-triterpene hydrocarbon (C30H50) with six double bonds, which is an intermediate in the biosynthesis of phytosterol or cholesterol in plants or animals. The sources of squalene and the main methods forsqualene production and determination are consideredin brief. Sunflower oil distillation cuts have been selected as the subject of the study, since they area promising secondary raw material for the industrial squalene production. The methods of sample preparation and quantification of squalene in sunflower oil distillation cuts applying gas chromatography in combination with mass spectrometry have been adapted. The aim of the studyis to create an integrated approach to determining the qualitative and quantitative content of squalene in distillation cuts of vegetable oils. To achieve the goal of the study, the following tasks have been solved: – Amethod of sample preparation of distillation cuts for determination of squalene has been adapted; – A method of qualitative and quantitative determination of squalene in distillation cuts has been modified. As a result of this study, a technique for sample preparation of distillation cuts was proposed as well as a method for the qualitative and quantitative (absolute calibrationmethod) determination of squalene in distillation cuts of sunflower oil. To implement the technique, a Kristall 5000 gas chromatograph equipped with a mass spectrometric detector was used. Squalene and background components were recorded using the NIST 11 mass spectral database.

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50

Liu, Pengpeng, Irwin King, Michael R. Lyu, and Jia Xu. "DDFlow: Learning Optical Flow with Unlabeled Data Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8770–77. http://dx.doi.org/10.1609/aaai.v33i01.33018770.

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We present DDFlow, a data distillation approach to learning optical flow estimation from unlabeled data. The approach distills reliable predictions from a teacher network, and uses these predictions as annotations to guide a student network to learn optical flow. Unlike existing work relying on handcrafted energy terms to handle occlusion, our approach is data-driven, and learns optical flow for occluded pixels. This enables us to train our model with a much simpler loss function, and achieve a much higher accuracy. We conduct a rigorous evaluation on the challenging Flying Chairs, MPI Sintel, KITTI 2012 and 2015 benchmarks, and show that our approach significantly outperforms all existing unsupervised learning methods, while running at real time.

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