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De R. Comparative dynamic optimization study of batch hydrothermal liquefaction of two microalgal strains for economic bio-oil production. BIORESOURCE TECHNOLOGY 2024; 398:130523. [PMID: 38437962 DOI: 10.1016/j.biortech.2024.130523] [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: 12/16/2023] [Revised: 02/27/2024] [Accepted: 03/01/2024] [Indexed: 03/06/2024]
Abstract
This work presents dynamic optimization strategies of batch hydrothermal liquefaction of two microalgal species, Aurantiochytrium sp. KRS101 and Nannochloropsis sp. to optimize the reactor temperature profiles. Three dynamic optimization problems are solved to maximize the endpoint biocrude yield, minimize the final time, and minimize the reactor thermal energy. The biocrude maximization and time minimization problems demonstrated 11% and 6.18% increment in the optimal biocrude yields and reduction of 78.2% and 61.66% in batch times compared to the base cases for the microalgae with higher lipid and protein fractions, respectively. The energy minimization problem revealed a significant reduction in the reactor thermal energies to generate the targeted biocrude yields compared to the biocrude maximization. Therefore, the identified optimal temperature trajectories outperformed the conventional fixed temperature profiles and could improve the overall economics of the batch bio-oil production from the algal-based biorefineries by significantly enhancing the reactor performance.
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Affiliation(s)
- Riju De
- Department of Chemical Engineering, Birla Institute of Technology and Science Pilani, K. K. Birla Goa campus, Zuarinagar, Goa 403726, India.
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Abstract
In recent years, the vigorous rise in computational intelligence has opened up new research ideas for solving chemical dynamic optimization problems, making the application of swarm-intelligence optimization techniques more and more widespread. However, the potential for algorithms with different performances still needs to be further investigated in this context. On this premise, this paper puts forward a universal swarm-intelligence dynamic optimization framework, which transforms the infinite-dimensional dynamic optimization problem into the finite-dimensional nonlinear programming problem through control variable parameterization. In order to improve the efficiency and accuracy of dynamic optimization, an improved version of the multi-strategy enhanced sparrow search algorithm is proposed from the application side, including good-point set initialization, hybrid algorithm strategy, Lévy flight mechanism, and Student’s t-distribution model. The resulting augmented algorithm is theoretically tested on ten benchmark functions, and compared with the whale optimization algorithm, marine predators algorithm, harris hawks optimization, social group optimization, and the basic sparrow search algorithm, statistical results verify that the improved algorithm has advantages in most tests. Finally, the six algorithms are further applied to three typical dynamic optimization problems under a universal swarm-intelligence dynamic optimization framework. The proposed algorithm achieves optimal results and has higher accuracy than methods in other references.
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Libotte GB, Lobato FS, Moura Neto FD, Platt GM. A Novel Reliability-Based Robust Design Multiobjective Optimization Formulation Applied in Chemical Engineering. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c04635] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Gustavo Barbosa Libotte
- Polytechnic Institute, Rio de Janeiro State University, 25, Bonfim Street, Nova Friburgo, 28625-570, Brazil
| | - Fran Sérgio Lobato
- Chemical Engineering Faculty, Federal University of Uberlândia, 2121, João Naves de Ávila Avenue, Uberlândia, 38400-902, Brazil
| | | | - Gustavo Mendes Platt
- School of Chemistry and Food, Federal University of Rio Grande, 3005, Cel. Francisco Borges de Lima Street, Santo Antônio da Patrulha, 95500-000, Brazil
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Enhanced Beetle Antennae Algorithm for Chemical Dynamic Optimization Problems’ Non-Fixed Points Discrete Solution. Processes (Basel) 2022. [DOI: 10.3390/pr10010148] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Dynamic optimization is an important research topic in chemical process control. A dynamic optimization method with good performance can reduce energy consumption and prompt production efficiency. However, the method of solving the problem is complicated in the establishment of the model, and the process of solving the optimal value has a certain degree of difficulty. Based on this, we proposed a non-fixed points discrete method of an enhanced beetle antennae optimization algorithm (EBSO) to solve this kind of problem. Firstly, we converted individual beetles into groups of beetles to search for the best and increase the diversity of the population. Secondly, we introduced a balanced direction strategy, which explored extreme values in new directions before the beetles updated their positions. Finally, a spiral flight mechanism was introduced to change the situation of the beetles flying straight toward the tentacles to prevent the traditional algorithm from easily falling into a certain local range and not being able to jump out. We applied the enhanced algorithm to four classic chemical problems. Meanwhile, we changed the equal time division method or unequal time division method commonly used to solve chemical dynamic optimization problems, and proposed a new interval distribution method—the non-fixed points discrete method, which can more accurately represent the optimal control trajectory. The comparison and analysis of the simulation test results with other algorithms for solving chemical dynamic optimization problems show that the EBSO algorithm has good performance to a certain extent, which further proves the effectiveness of the EBSO algorithm and has a better optimization ability.
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De R, Bhartiya S, Shastri Y. Parameter estimation and optimal control of a batch transesterification reactor: An experimental study. Chem Eng Res Des 2020. [DOI: 10.1016/j.cherd.2020.02.027] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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6
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Gas hydrodynamics of a novel MTO high-speed loop reactor: The bypassing and backmixing along with average residence time. POWDER TECHNOL 2020. [DOI: 10.1016/j.powtec.2019.09.032] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Xiao L, Liu X, He S. An Adaptive Pseudospectral Method for Constrained Dynamic Optimization Problems in Chemical Engineering. Chem Eng Technol 2016. [DOI: 10.1002/ceat.201600281] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Liu P, Li G, Liu X, Zhang Z. Novel non-uniform adaptive grid refinement control parameterization approach for biochemical processes optimization. Biochem Eng J 2016. [DOI: 10.1016/j.bej.2016.03.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Zhang P, Chen H, Liu X, Zhang Z. An iterative multi-objective particle swarm optimization-based control vector parameterization for state constrained chemical and biochemical engineering problems. Biochem Eng J 2015. [DOI: 10.1016/j.bej.2015.07.004] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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10
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Zhang P, Liu X, Ma L. Optimal Control Vector Parameterization Approach with a Hybrid Intelligent Algorithm for Nonlinear Chemical Dynamic Optimization Problems. Chem Eng Technol 2015. [DOI: 10.1002/ceat.201400796] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Liu P, Li G, Liu X. Fast engineering optimization: A novel highly effective control parameterization approach for industrial dynamic processes. ISA TRANSACTIONS 2015; 58:248-254. [PMID: 26117286 DOI: 10.1016/j.isatra.2015.06.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2014] [Revised: 05/18/2015] [Accepted: 06/02/2015] [Indexed: 06/04/2023]
Abstract
Control vector parameterization (CVP) is an important approach of the engineering optimization for the industrial dynamic processes. However, its major defect, the low optimization efficiency caused by calculating the relevant differential equations in the generated nonlinear programming (NLP) problem repeatedly, limits its wide application in the engineering optimization for the industrial dynamic processes. A novel highly effective control parameterization approach, fast-CVP, is first proposed to improve the optimization efficiency for industrial dynamic processes, where the costate gradient formulae is employed and a fast approximate scheme is presented to solve the differential equations in dynamic process simulation. Three well-known engineering optimization benchmark problems of the industrial dynamic processes are demonstrated as illustration. The research results show that the proposed fast approach achieves a fine performance that at least 90% of the computation time can be saved in contrast to the traditional CVP method, which reveals the effectiveness of the proposed fast engineering optimization approach for the industrial dynamic processes.
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Affiliation(s)
- Ping Liu
- State Key Laboratory of Industrial Control Technology, Department of Control Science & Engineering, Zhejiang University, Hangzhou 310027, China
| | - Guodong Li
- State Key Laboratory of Industrial Control Technology, Department of Control Science & Engineering, Zhejiang University, Hangzhou 310027, China
| | - Xinggao Liu
- State Key Laboratory of Industrial Control Technology, Department of Control Science & Engineering, Zhejiang University, Hangzhou 310027, China.
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Zhou Y, Liu X. Control Parameterization-Based Adaptive Particle Swarm Approach for Solving Chemical Dynamic Optimization Problems. Chem Eng Technol 2014. [DOI: 10.1002/ceat.201300474] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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A Real-time Updated Model Predictive Control Strategy for Batch Processes Based on State Estimation. Chin J Chem Eng 2014. [DOI: 10.1016/s1004-9541(14)60057-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Yan C, Lu C, Zhang Y, Wang D, Liu M. Profiles of solid fraction and heterogeneous phase structure in a gas–solid airlift loop reactor. Chem Eng Sci 2010. [DOI: 10.1016/j.ces.2010.01.005] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Yan C, Lu C, Liu Y, Cao R, Shi M. Hydrodynamics in airlift loop section of petroleum coke combustor. POWDER TECHNOL 2009. [DOI: 10.1016/j.powtec.2008.12.005] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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