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Abdi J, Mazloom G, Hadavimoghaddam F, Hemmati-Sarapardeh A, Esmaeili-Faraj SH, Bolhasani A, Karamian S, Hosseini S. Estimation of the flow rate of pyrolysis gasoline, ethylene, and propylene in an industrial olefin plant using machine learning approaches. Sci Rep 2023; 13:14081. [PMID: 37640807 PMCID: PMC10462638 DOI: 10.1038/s41598-023-41273-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 08/24/2023] [Indexed: 08/31/2023] Open
Abstract
Light olefins, as the backbone of the chemical and petrochemical industries, are produced mainly via steam cracking route. Prediction the of effects of operating variables on the product yield distribution through the mechanistic approaches is complex and requires long time. While increasing in the industrial automation and the availability of the high throughput data, the machine learning approaches have gained much attention due to the simplicity and less required computational efforts. In this study, the potential capability of four powerful machine learning models, i.e., Multilayer perceptron (MLP) neural network, adaptive boosting-support vector regression (AdaBoost-SVR), recurrent neural network (RNN), and deep belief network (DBN) was investigated to predict the product distribution of an olefin plant in industrial scale. In this regard, an extensive data set including 1184 actual data points were gathered during four successive years under various practical conditions. 24 varying independent parameters, including flow rates of different feedstock, numbers of active furnaces, and coil outlet temperatures, were chosen as the input variables of the models and the outputs were the flow rates of the main products, i.e., pyrolysis gasoline, ethylene, and propylene. The accuracy of the models was assessed by different statistical techniques. Based on the obtained results, the RNN model accurately predicted the main product flow rates with average absolute percent relative error (AAPRE) and determination coefficient (R2) values of 1.94% and 0.97, 1.29% and 0.99, 0.70% and 0.99 for pyrolysis gasoline, propylene, and ethylene, respectively. The influence of the various parameters on the products flow rate (estimated by the RNN model) was studied by the relevancy factor calculation. Accordingly, the number of furnaces in service and the flow rates of some feedstock had more positive impacts on the outputs. In addition, the effects of different operating conditions on the propylene/ethylene (P/E) ratio as a cracking severity factor were also discussed. This research proved that intelligent approaches, despite being simple and straightforward, can predict complex unit performance. Thus, they can be efficiently utilized to control and optimize different industrial-scale units.
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Affiliation(s)
- Jafar Abdi
- Faculty of Chemical and Materials Engineering, Shahrood University of Technology, Shahrood, Iran.
| | - Golshan Mazloom
- Department of Chemical Engineering, Faculty of Engineering, University of Mazandaran, Babolsar, Iran
| | - Fahimeh Hadavimoghaddam
- Institute of Unconventional Oil & Gas, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China
- Ufa State Petroleum Technological University, Ufa, 450064, Russia
| | - Abdolhossein Hemmati-Sarapardeh
- Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
- State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum (Beijing), Beijing, China.
| | | | - Akbar Bolhasani
- Research and Development Center, Jam Petrochemical Company, Bushehr, 1434853114, Iran
| | - Soroush Karamian
- Research and Development Center, Jam Petrochemical Company, Bushehr, 1434853114, Iran
| | - Shahin Hosseini
- Research and Development Center, Jam Petrochemical Company, Bushehr, 1434853114, Iran
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Zhao L, Wang T, Zhang Y, Tang Z. Modeling Fischer–Tropsch to Olefins in Pilot Slurry Process with a Method of Multiscale Bubbles Hybrid Injection. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c02995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Luhaibo Zhao
- Shanghai Advanced Research Institute (SARI), Chinese Academy of Sciences (CAS), Shanghai201210, P. R. China
| | - Teng Wang
- East China University of Science and Technology, Shanghai200237, P. R. China
| | - Yaheng Zhang
- Shanghai Advanced Research Institute (SARI), Chinese Academy of Sciences (CAS), Shanghai201210, P. R. China
| | - Zhiyong Tang
- Shanghai Advanced Research Institute (SARI), Chinese Academy of Sciences (CAS), Shanghai201210, P. R. China
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Pathoumthong K, Ratanamalaya P, Limtrakul S, Vatanatham T, Ramachandran PA. Kinetics, Mass Transfer, and Reactor Scaling Up in Production of Direct Dimethyl Ether. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c03634] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Affiliation(s)
- Kainakhone Pathoumthong
- Department of Chemical Engineering, Faculty of Engineering, Kasetsart University, Bangkok10900, Thailand
- Center of Excellence on Petrochemical and Materials Technology, Department of Chemical Engineering, Faculty of Engineering, Kasetsart University, Bangkok10900, Thailand
- Center for Advanced Studies in Industrial Technology, Faculty of Engineering, Kasetsart University, Bangkok10900, Thailand
| | - Putong Ratanamalaya
- Department of Chemical Engineering, Faculty of Engineering, Kasetsart University, Bangkok10900, Thailand
- Center of Excellence on Petrochemical and Materials Technology, Department of Chemical Engineering, Faculty of Engineering, Kasetsart University, Bangkok10900, Thailand
- Center for Advanced Studies in Industrial Technology, Faculty of Engineering, Kasetsart University, Bangkok10900, Thailand
| | - Sunun Limtrakul
- Department of Chemical Engineering, Faculty of Engineering, Kasetsart University, Bangkok10900, Thailand
- Center of Excellence on Petrochemical and Materials Technology, Department of Chemical Engineering, Faculty of Engineering, Kasetsart University, Bangkok10900, Thailand
- Center for Advanced Studies in Industrial Technology, Faculty of Engineering, Kasetsart University, Bangkok10900, Thailand
| | - Terdthai Vatanatham
- Department of Chemical Engineering, Faculty of Engineering, Kasetsart University, Bangkok10900, Thailand
- Center of Excellence on Petrochemical and Materials Technology, Department of Chemical Engineering, Faculty of Engineering, Kasetsart University, Bangkok10900, Thailand
- Center for Advanced Studies in Industrial Technology, Faculty of Engineering, Kasetsart University, Bangkok10900, Thailand
| | - Palghat A. Ramachandran
- Department of Energy, Environmental & Chemical Engineering, Washington University in St. Louis, St. Louis, Missouri, 63130, United States
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Abdi J, Mazloom G. Machine learning approaches for predicting arsenic adsorption from water using porous metal-organic frameworks. Sci Rep 2022; 12:16458. [PMID: 36180503 PMCID: PMC9525301 DOI: 10.1038/s41598-022-20762-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 09/19/2022] [Indexed: 11/21/2022] Open
Abstract
Arsenic in drinking water is a serious threat for human health due to its toxic nature and therefore, its eliminating is highly necessary. In this study, the ability of different novel and robust machine learning (ML) approaches, including Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Gradient Boosting Decision Tree, and Random Forest was implemented to predict the adsorptive removal of arsenate [As(V)] from wastewater over 13 different metal–organic frameworks (MOFs). A large experimental dataset was collected under various conditions. The adsorbent dosage, contact time, initial arsenic concentration, adsorbent surface area, temperature, solution pH, and the presence of anions were considered as input variables, and adsorptive removal of As(V) was selected as the output of the models. The developed models were evaluated using various statistical criteria. The obtained results indicated that the LightGBM model provided the most accurate and reliable response to predict As(V) adsorption by MOFs and possesses R2, RMSE, STD, and AAPRE (%) of 0.9958, 2.0688, 0.0628, and 2.88, respectively. The expected trends of As(V) removal with increasing initial concentration, solution pH, temperature, and coexistence of anions were predicted reasonably by the LightGBM model. Sensitivity analysis revealed that the adsorption process adversely relates to the initial As(V) concentration and directly depends on the MOFs surface area and dosage. This study proves that ML approaches are capable to manage complicated problems with large datasets and can be affordable alternatives for expensive and time-consuming experimental wastewater treatment processes.
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Affiliation(s)
- Jafar Abdi
- Faculty of Chemical and Materials Engineering, Shahrood University of Technology, Shahrood, Iran.
| | - Golshan Mazloom
- Department of Chemical Engineering, Faculty of Engineering, University of Mazandaran, Babolsar, Iran
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