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A microscopic computational model based on particle dynamics and evolutionary algorithm for the prediction of gas solubility in polymers. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.120169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Logic-based data-driven operational risk model for augmented downhole petroleum production systems. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Mamudu A, Khan F, Zendehboudi S, Adedigba S. A Connectionist Model for Dynamic Economic Risk Analysis of Hydrocarbons Production Systems. RISK ANALYSIS : AN OFFICIAL PUBLICATION OF THE SOCIETY FOR RISK ANALYSIS 2022; 42:1541-1570. [PMID: 34784431 DOI: 10.1111/risa.13829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 09/09/2021] [Accepted: 09/11/2021] [Indexed: 06/13/2023]
Abstract
This study presents a connectionist model for dynamic economic risk evaluation of reservoir production systems. The proposed dynamic economic risk modeling strategy combines evidence-based outcomes from a Bayesian network (BN) model with the dynamic risks-based results produced from an adaptive loss function model for reservoir production losses/dynamic economic risks assessments. The methodology employs a multilayer-perceptron (MLP) model, a loss function model; it integrates an early warning index system (EWIS) of oilfield block with a BN model for process modeling. The model evaluates the evidence-based economic consequences of the production losses and analyzes the statistical disparities of production predictions using an EWIS-assisted BN model and the loss function model at the same time. The proposed methodology introduces an innovative approach that effectively minimizes the potential for dynamic economic risks. The model predicts real-time daily production/dynamic economic losses. The connectionist model yields an encouraging overall predictive performance with average errors of 1.954% and 1.957% for the two case studies: cases 1 and 2, respectively. The model can determine transitional/threshold production values for adequate reservoir management toward minimal losses. The results show minimum average daily dynamic economic losses of $267,463 and $146,770 for cases 1 and 2, respectively. It is a multipurpose tool that can be recommended for the field operators in petroleum reservoir production management related decision making.
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Affiliation(s)
- Abbas Mamudu
- Centre for Risk, Integrity and Safety Engineering (CRISE), Faculty of Engineering and Applied Science, Memorial University, St. John's, Newfoundland and Labrador, Canada
- Department of Process Engineering, Faculty of Engineering and Applied Science, Memorial University, St. John's, Canada
| | - Faisal Khan
- Centre for Risk, Integrity and Safety Engineering (CRISE), Faculty of Engineering and Applied Science, Memorial University, St. John's, Newfoundland and Labrador, Canada
- Mary Kay O'Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX, USA
| | - Sohrab Zendehboudi
- Department of Process Engineering, Faculty of Engineering and Applied Science, Memorial University, St. John's, Canada
| | - Sunday Adedigba
- Centre for Risk, Integrity and Safety Engineering (CRISE), Faculty of Engineering and Applied Science, Memorial University, St. John's, Newfoundland and Labrador, Canada
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Ahandani MA, Abbasfam J, Kharrati H. Parameter identification of permanent magnet synchronous motors using quasi-opposition-based particle swarm optimization and hybrid chaotic particle swarm optimization algorithms. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03223-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Chen H, Zeng M, Zhang H, Chen B, Guan L, Li M. Prediction of Carbon Dioxide Solubility in Polymers Based on Adaptive Particle Swarm Optimization and Least Squares Support Vector Machine. ChemistrySelect 2022. [DOI: 10.1002/slct.202104447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Huijie Chen
- College of Physics and Electronic Information Gannan Normal University Ganzhou Jiangxi 341000 China
| | - Ming Zeng
- College of Physics and Electronic Information Gannan Normal University Ganzhou Jiangxi 341000 China
| | - Hang Zhang
- College of Physics and Electronic Information Gannan Normal University Ganzhou Jiangxi 341000 China
| | - Bingsheng Chen
- College of Physics and Electronic Information Gannan Normal University Ganzhou Jiangxi 341000 China
| | - Lixin Guan
- College of Physics and Electronic Information Gannan Normal University Ganzhou Jiangxi 341000 China
| | - Mengshan Li
- College of Physics and Electronic Information Gannan Normal University Ganzhou Jiangxi 341000 China
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Wu Y, Zhang H, Li MS, Sheng S, Wang J, Wu FA. A double-population chaotic self-adaptive evolutionary dynamics model for the prediction of supercritical carbon dioxide solubility in polymers. ROYAL SOCIETY OPEN SCIENCE 2022; 9:211419. [PMID: 35116155 PMCID: PMC8767190 DOI: 10.1098/rsos.211419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Accepted: 11/25/2021] [Indexed: 05/03/2023]
Abstract
Solubility of gas in polymers is an important physico-chemical property of foam materials and widely used in the preparation and modification of new materials. Under the conditions of high temperature and high pressure, the dissolution process is a nonlinear, non-equilibrium and dynamic process, so it is difficult to establish an accurate solubility calculation model. Inspired by particle dynamics and evolutionary algorithm, this paper proposes a hybrid model based on chaotic self-adaptive particle dynamics evolutionary algorithm (CSA-PD-EA), which can use the iterative process of particles in evolutionary algorithms at the dynamic level to simulate the mutual diffusion process of molecules during dissolution. The predicted solubility of supercritical CO2 in poly(d,l-lactide-co-glycolide), poly(l-lactide) and poly(vinyl acetate) indicated that the comprehensive prediction performance of the CSA-PD-EA model was high. The calculation error and correlation coefficient were, respectively, 0.3842 and 0.9187. The CSA-PD-EA model showed prominent advantages in accuracy, efficiency and correlation over other computational models, and its calculation time was 4.144-15.012% of that of other dynamic models. The CSA-PD-EA model has wide application prospects in the computation of physical and chemical properties and can provide the basis for the theoretical calculation of multi-scale complex systems in chemistry, materials, biology and physics.
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Affiliation(s)
- Yan Wu
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212018, People's Republic of China
- School of Mathematics and Computer Science, Gannan Normal University, Ganzhou Jiangxi 341000, People's Republic of China
- Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu 212018, People's Republic of China
| | - Hang Zhang
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou Jiangxi 341000, People's Republic of China
| | - Meng-shan Li
- College of Physics and Electronic Information, Gannan Normal University, Ganzhou Jiangxi 341000, People's Republic of China
| | - Sheng Sheng
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212018, People's Republic of China
- Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu 212018, People's Republic of China
| | - Jun Wang
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212018, People's Republic of China
- Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu 212018, People's Republic of China
| | - Fu-an Wu
- School of Biotechnology, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212018, People's Republic of China
- Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang, Jiangsu 212018, People's Republic of China
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