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A further study in the prediction of viscosity for Iranian crude oil reservoirs by utilizing a robust radial basis function (RBF) neural network model. Neural Comput Appl 2023. [DOI: 10.1007/s00521-023-08256-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
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Li M, Zhao L, Jin S, Li D, Huang J, Liu J. Process schemes of ethanol coupling to C4 olefins based on a genetic algorithm for back propagation neural network optimization. Heliyon 2022; 8:e12301. [PMID: 36578395 PMCID: PMC9791839 DOI: 10.1016/j.heliyon.2022.e12301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 09/06/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
C4 olefin is an important feedstock for the chemical industry. Designing an effective and stable industrial process for preparing C4 olefin from renewable ethanol is crucial for further sustainable chemical production. In this study, a comprehensive evaluation system of an experimental scheme was constructed based on the Analytic Hierarchy Process/Entropy Weight Method-Technique for Order Preference by Similarity to Ideal Solution (AHP/EWM-TOPSIS) and Chemical production indicators. Using this evaluation system, a Back Propagation Neural Network (BPNN) based on a Genetic Algorithm (GA) was constructed after simulating C4 olefin production conditions using the Improved Mixed Congruential method. Subsequently, the production scheme with the highest evaluation score was determined when the temperature was not limited and when the temperature was lower than 350°C through a series of mathematical models. Overall, our mathematical models provide guidance for the commercial production of ethanol to butene and effectively reduce the risk of scaling up the chemical process to pilot or industrial scale.
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Affiliation(s)
- Minghan Li
- School of Life and Pharmaceutical Sciences, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
- Panjin Campus, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
| | - Lingling Zhao
- Panjin Campus, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
| | - Shuo Jin
- School of Life and Pharmaceutical Sciences, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
- Panjin Campus, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
| | - Danlu Li
- School of Life and Pharmaceutical Sciences, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
- Panjin Campus, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
| | - Jingyi Huang
- Panjin Campus, Dalian University of Technology, 2 Dagong Road, Panjin, Liaoning 124221, PR China
| | - Jiaxin Liu
- China Nuclear Power Engineering Co., Ltd., 117 North West Third Ring Road, Beijing 100840, PR China
- School of Chemical Engineering, Dalian University of Technology, 2 Linggong Road, Dalian, Liaoning 116024, PR China
- Corresponding author at: China Nuclear Power Engineering Co., Ltd., 117 North West Third Ring Road, Beijing 100840, PR China.
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Euldji I, SI-MOUSSA C, HAMADACHE M, BENKORTBI O. QSPR Modelling of The Solubility of Drug and Drug‐Like Compounds in Supercritical Carbon Dioxide. Mol Inform 2022; 41:e2200026. [DOI: 10.1002/minf.202200026] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 04/03/2022] [Indexed: 11/05/2022]
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Establishment of the Predicting Models of the Dyeing Effect in Supercritical Carbon Dioxide Based on the Generalized Regression Neural Network and Back Propagation Neural Network. Processes (Basel) 2020. [DOI: 10.3390/pr8121631] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
With the growing demand of supercritical carbon dioxide (SC-CO2) dyeing, it is important to precisely predict the dyeing effect of supercritical carbon dioxide. In this work, Generalized Regression Neural Network (GRNN) and Back Propagation Neural Network (BPNN) models have been employed to predict the dyeing effect of SC-CO2. These two models have been constructed based on published experimental data and calculated values. A total of 386 experimental data sets were used in the present work. In GRNN and BPNN models, two input parameters, such as temperature, pressure, dye stuff types, carrier types and dyeing time, were selected for the input layer and one variable, K/S value or dye-uptake, was used in the output layer. It was found that the values of mean-relative-error (MRE) for BPNN model and for GRNN model are 3.27–6.54% and 1.68–3.32%, respectively. The results demonstrate that both BPNN and GPNN models can accurately predict the effect of supercritical dyeing but the former is better than the latter.
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Determination of Anthraquinone Violet 3RN solubility in supercritical carbon dioxide with/without co-solvent: Experimental data and modeling (empirical and thermodynamic models). Chem Eng Res Des 2020. [DOI: 10.1016/j.cherd.2020.04.026] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Zhu W, Liu X, Hou X, Hu J, Diao Z. Application of machine learning to process simulation of n-pentane cracking to produce ethylene and propene. Chin J Chem Eng 2020. [DOI: 10.1016/j.cjche.2020.01.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Semi-empirical correlation of solid solute solubility in supercritical carbon dioxide: Comparative study and proposition of a novel density-based model. CR CHIM 2018. [DOI: 10.1016/j.crci.2018.02.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Yang G, Li Z, Shao Q, Feng N. Measurement and correlation study of silymarin solubility in supercritical carbon dioxide with and without a cosolvent using semi-empirical models and back-propagation artificial neural networks. Asian J Pharm Sci 2017; 12:456-463. [PMID: 32104358 PMCID: PMC7032250 DOI: 10.1016/j.ajps.2017.04.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2017] [Accepted: 04/29/2017] [Indexed: 12/05/2022] Open
Abstract
The solubility data of compounds in supercritical fluids and the correlation between the experimental solubility data and predicted solubility data are crucial to the development of supercritical technologies. In the present work, the solubility data of silymarin (SM) in both pure supercritical carbon dioxide (SCCO2) and SCCO2 with added cosolvent was measured at temperatures ranging from 308 to 338 K and pressures from 8 to 22 MPa. The experimental data were fit with three semi-empirical density-based models (Chrastil, Bartle and Mendez-Santiago and Teja models) and a back-propagation artificial neural networks (BPANN) model. Interaction parameters for the models were obtained and the percentage of average absolute relative deviation (AARD%) in each calculation was determined. The correlation results were in good agreement with the experimental data. A comparison among the four models revealed that the experimental solubility data were more fit with the BPANN model with AARDs ranging from 1.14% to 2.15% for silymarin in pure SCCO2 and with added cosolvent. The results provide fundamental data for designing the extraction of SM or the preparation of its particle using SCCO2 techniques.
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Affiliation(s)
- Gang Yang
- Department of Pharmaceutical Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Zhe Li
- Department of Pharmaceutical Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
| | - Qun Shao
- Open Innovation, University of Bradford, West Yorkshire, BD7 1DP, UK
| | - Nianping Feng
- Department of Pharmaceutical Sciences, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, China
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Yuan P, Zhang B, Mao Z. A self-tuning control method for Wiener nonlinear systems and its application to process control problems. Chin J Chem Eng 2017. [DOI: 10.1016/j.cjche.2016.07.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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