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Sustainable crude oil desulfurization: A multi-objective fuzzy optimization for pollution prevention

PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2024年第PartA期190卷 913-920页

作者： Correa, Stephen S. Arbilo, Angel Nicole V. Alviar, Kate Andre T. Choi, Angelo Earvin Sy De La Salle Univ Dept Chem Engn 2401 Taft Ave Manila 0922 Philippines

The ultrasonic-assisted oxidative desulfurization (UAOD) process is employed to mitigate the environmental impact of sulfur emissions. Previous studies have focused on evaluating the efficiency of crude oil UAOD. This research assesses the desulfurization efficiency encompassing irradiation time, ultrasonic power, and oxidant amount, as well as the corresponding operational expenses, to ascertain the optimal conditions. The research employed a multi-objective fuzzy optimization (MOFO) method to assess the operational expenses of crude oil UAOD. It established the upper and lower limits and subsequently optimized the operational costs and desulfurization efficiency while accounting for uncertainties. The fuzzy model optimized the level of satisfaction on a scale from 0 (unsatisfied) to 1 (satisfied) using the max-min aggregation. The degree of satisfaction slightly decreased to 55.49 %, achieved under optimal conditions of 739.70 W ultrasonic power, 6.70 min irradiation, and 8.47 mL oxidant. This configuration yielded a desulfurization efficiency (YA) Y A ) of 63.24% at 2.02 USD/L. Compared to existing literature, the desulfurization of crude oil was found to be less efficient and less expensive. The outcomes presented by MOFO showcase economic feasibility with reduced overall operating expenses and simplified process conditions and provide valuable insights for optimizing potential large-scale UAOD processes.

关键词： Crude oil Response surface methodology Ultrasonic-assisted oxidative desulfurization Pareto frontier epsilon-constraint multi-objective fuzzy optimization

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Advancing gasoline desulfurization: multi-objective fuzzy optimization in systems technology

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HELIYON 2024年第11期10卷 e32346页

作者： Correa, Stephen S. Alviar, Kate Andre T. Arbilo, Angel Nicole V. Choi, Angelo Earvin Sy De La Salle Univ Dept Chem Engn 2401 Taft Ave Manila 0922 Philippines

Ultrasonic-assisted oxidative desulfurization (UAOD) is utilized to lessen environmental problems due to sulfur emissions. The process uses immiscible polar solvents and ultrasonic waves to enhance desulfurization efficiency. Prior research focused on comparing the effectiveness of UAOD for gasoline using response surface methodology. This study evaluates the desulfurization efficiency and operating costs, including ultrasonic power, irradiation time, and oxidant amount to determine optimal conditions. The study used a multi-objective fuzzy optimization (MOFO) approach to evaluate the economic viability of UAOD for gasoline. It identified upper and lower boundaries and then optimized the desulfurization efficiency and operating costs while considering uncertainty errors. The fuzzy model employed max-min aggregation to optimize the degree of satisfaction on a scale from 0 (unsatisfied) to 1 (satisfied). Optimal conditions for gasoline UAOD were found at 445.43 W ultrasonic power, 4.74 min irradiation time, and 6.73 mL oxidant, resulting in a 66.79 % satisfaction level. This yielded a 78.64 % desulfurization efficiency (YA) at an operating cost of 13.49 USD/L. Compared to existing literature, gasoline desulfurization was less efficient and less costly. The solutions provided by MOFO demonstrate not only economic viability through decreased overall operating costs and simplified process conditions, but also offer valuable insights for optimizing prospective future industrial-scale UAOD processes.

关键词： Gasoline Ultrasonic assisted oxidative desulfurization Response surface methodology multi-objective fuzzy optimization epsilon-constraint Pareto frontier

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Integrated energy system scheduling model based on non-complete interval multi-objective fuzzy optimization

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RENEWABLE ENERGY 2023年 218卷

作者： Yang, Xiaohui Wang, Xiaopeng Deng, Yeheng Mei, Linghao Deng, Fuwei Zhang, Zhonglian Nanchang Univ Coll Informat Engn Nanchang 330031 Jiangxi Peoples R China

Integrated energy system (IES) plays an important role in realizing efficient use of clean energy. Considering the influence of various uncertainties, an IES scheduling model based on non-complete interval multi-objective fuzzy optimization is proposed to improve the operation efficiency of IES and to reduce the external impacts of IES. Firstly, the uncertainties of renewable energy, load and demand response are described by interval numbers, and a multi-objective optimization model considering satisfaction with electricity is proposed to guarantee the reduction of user experience loss in optimizing IES electricity load. Secondly, by analyzing the shortcomings of the traditional interval optimization methods and improving them, a non-complete interval multi-objective fuzzy optimization model is proposed in the day-ahead scheduling stage, which improves the operational efficiency of IES. In addition, an optimum interval cross section search model is proposed in the real-time scheduling stage to further improve the practicality of the proposed interval optimization model. The simulation results under typical day in summer show that compared with the traditional optimization model, the proposed model improves the economy, environmental protection, reliability and renewable energy consumption rate of IES by 2.51%, 13.07%, 5.03% and 8.17% respectively, which proves the effectiveness of the proposed model in optimization performance.

关键词： Integrated energy system Renewable energy consumption Uncertainty of IES Non-complete interval optimization multi-objective fuzzy optimization

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Hierarchical multi-objective fuzzy Collaborative optimization of Integrated Energy System under Off-Design Performance

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ENERGIES 2019年第5期12卷 830-830页

作者： Zhong, Yongjie Zhou, Hongwei Zong, Xuanjun Xu, Zhou Sun, Yonghui Hohai Univ Coll Energy & Elect Engn Nanjing 210098 Jiangsu Peoples R China State Grid Jiangsu Elect Power Co Ltd Econ Res Inst Nanjing 210008 Jiangsu Peoples R China

In order to solve the frequently occurred nonlinear working characteristics problem of the devices in integrated energy system (IES), where this nonlinear problem causes the offset of operation characteristics and design points of those devices, in this paper, the hierarchical multi-objective fuzzy collaborative optimization model of IES under off-design performance is proposed. Firstly, the high-order nonlinear models of devices under off-design performance, including fuel cell (FC), gas turbine (GT), absorption lithium bromide chiller (ABS), etc., are presented considering renewable energy and diversified storage devices, which can more accurately describe the actual working condition of devices. Secondly, according to the needs of different engineering application modes, the lower level collaborative optimization model of energy hub (EH) with economic, eco-friendly, primary energy saving, and renewable energy accommodation rate as optimization objectives is proposed, which can well adapt to and reflect real world energy system. Thirdly, based on the relationship between master and slave dispatch centers, the upper level modeling and optimal scheduling model of IES consisting of coupling power system, natural gas system, and heat system is proposed. Then, a multi-objective fuzzy collaborative optimization model for EH and IES is developed, where continuous differentiable Sigmoid function is taken as the membership function. Finally, simulation results show that the proposed models and optimal dispatch method can effectively solve the frequently occurred non-linear working characteristics problem of the devices in IES. The optimal results are suitable for planning, calculation, operation, dispatch of IES, which can reasonably reflect the operation characteristics of IES.

关键词： integrated energy system (IES) energy hub (EH) multi-objective fuzzy optimization off-design performance multi-energy flow device

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multi-objective fuzzy-optimization Control Strategy of Rotor Converter for Doubly-Fed Induction Generators under Grid Voltage Unbalance

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2018年第11期13卷 1551-1560页

作者： Zhang, Wenjuan Ma, Haomiao Baoji Univ Arts & Sci Sch Elect Engn Baoji Shaanxi Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian Shaanxi Peoples R China

Under unbalanced grid voltages, the output active power, reactive power, and current in the stator of a doubly fed induction generator (DFIG) exhibit harmonic frequency fluctuations and distortion. Conventional control strategies to eliminate these effects only focus on one control objective at a time. This paper proposes a genetic algorithm-based multi-objective coordinated control strategy to eliminate multiple undesired effects in DFIGs under unbalanced power supply conditions. In order to enhance its low-voltage ride-through (LVRT) capability and effectively reduce the impact of the grid current on the power grid, this strategy first introduces adjustment coefficients when calculating the directive current of the rotor, and constructs a unified analytic expression for multiple control objectives. Next, the multi-objective optimization model is constructed using the minimization of the fluctuations in active and reactive powers as the control objective and the total harmonic distortion (THD) of the stator current as the constraint condition. Finally, the adjustment coefficients are optimized using the multi-objective genetic algorithm. From the perspective of the overall requirements of generators under a voltage unbalance fault condition, the optimized adjustment coefficients aim to limit the THD of the stator current within an acceptable safety range while simultaneously minimizing the fluctuations in the active and reactive powers. The effectiveness and feasibility of the proposed control strategy are proven by both simulation and experimental results. (C) 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： doubly fed induction generator (DFIG) voltage unbalance multi-objective fuzzy optimization genetic algorithm

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Bi-level multi-objective fuzzy design optimization of energy supply systems aided by problem-specific heuristics

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ENERGY 2017年 137卷 1231-1251页

作者： Stojiljkovic, Mirko M. Univ Nis Fac Mech Engn Nis Aleksandra Medvedeva 14 Nish 18000 Serbia

multi-objective design and operation optimization of energy supply systems with respect to costs, energy efficiency and environmental indicators is a very important, yet difficult and challenging task. The assumption of cost-optimal operation may impose the necessity for bi-level problems. This paper presents a methodology to formulate and solve multi-objective bi-level optimization problems where upper-level decision makers decide on design and policy, while plant operation is defined at the lower level. It can be used to assess energy consumption or emission reduction potential corresponding to cost optimal operation. The methodology applies conceptual decomposition to the outer design- and inner operation-optimization problems. The first is solved with the scatter search method and the second using mixed integer linear programming. Scatter search is a metaheuristic method convenient to integrate problem-specific heuristics into the process and systematically take advantage of the designer's knowledge. The fuzzy approach considers multiple design objectives and helps formulate heuristic rules based on the decision criteria satisfaction rates. The case study illustrates the design of a trigeneration system for an urban settlement. The design objectives are: total costs, primary energy and greenhouse gases emission. Cost-optimal operation is presumed. The optimal plant design and operation are realistic and mostly consistent with the literature. Compared to the cost-optimal solution, other objectives might lead to reduced primary energy consumption for 7.21% or lower emission for 91.85% with 43.35% and 83.1% larger costs. The results are compared with the ones obtained with customary methods. It is demonstrated that the methodology is applicable and convenient for the considered class of problems. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Bi-level optimization Energy supply systems design Metaheuristics Mixed integer linear programming multi-objective fuzzy optimization Problem-specific heuristics

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multi-objective fuzzy-optimization of Crowbar Resistances for the Low-Voltage Ride-through of Doubly Fed Induction Wind Turbine Generation Systems

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JOURNAL OF POWER ELECTRONICS 2015年第4期15卷 1119-1130页

作者： Zhang, Wenjuan Ma, Haomiao Zhang, Junli Chen, Lingling Qu, Yang Baoji Univ Arts & Sci Dept Elect Engn Baoji Shaanxi Peoples R China Xian Univ Technol Coll Automat & Informat Engn Xian Shaanxi Peoples R China

This study investigates the multi-objective fuzzy optimization of crowbar resistance for the doubly fed induction generator (DFIG) low-voltage ride-through (LVRT). By integrating the crowbar resistance of the crowbar circuit as a decision variable, a multi-objective model for crowbar resistance value optimization has been established to minimize rotor overcurrent and to simultaneously reduce the DFIG reactive power absorbed from the grid during the process of LVRT. A multi-objective genetic algorithm (MOGA) is applied to solve this optimization problem. In the proposed GA, the value of the crowbar resistance is represented by floating-point numbers in the GA population. The MOGA emphasizes the non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy-set-theory-based is employed to obtain the best solution. The proposed approach has been evaluated on a 3 MW DFIG LVRT. Simulation results show the effectiveness of the proposed approach for solving the crowbar resistance multi-objective optimization problem in the DFIG LVRT.

关键词： Crowbar resistance Doubly-fed induction generator (DFIG) Genetic algorithm (GA) Low voltage ride through (LVRT) multi-objective fuzzy optimization objective functions

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Robust stochastic low-carbon optimal dispatch of park-integrated energy system with multiple uncertainties from source and load

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Zong, Xuanjun Zou, Sheng Zhou, Hongwei Dou, Xiaobo State Grid Jiangsu Elect Power Co Ltd Econ Res Inst Nanjing Peoples R China Southeast Univ Sch Elect Engn Nanjing Peoples R China

To realize the cascaded utilization of energy, improve the effective utilization of energy, and further reduce the carbon emissions of integrated energy systems a robust stochastic low-carbon optimal dispatch model with economy, environmental protection and reliability is developed for a park-integrated energy system wherein the multiple uncertainties brought by source and load are fully considered. First, a two-stage robust optimization algorithm is employed to handle uncertain wind power generation. A multi-case analysis method for the uncertainties of photovoltaics and load is proposed based on an improved centralized reduction algorithm. Then, considering the depreciation of the weighted average of the comprehensive operation cost, carbon emissions, and energy undersupply rate, a robust stochastic optimal dispatch model can be derived and efficiently solved by using a multi-objective fuzzy optimization algorithm with an improved membership function. Finally, by comparing the four cases, the simulation results show that the computational complexity and calculation time of the system can be reduced, the trimming result errors can be decreased, and a balance between economy, environmental protection, reliability, and robustness can be achieved.

关键词： low carbon optimal dispatch robust optimization multiple uncertainties of source and load improved centralized reduction multi-objective fuzzy optimization

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A Combined Algorithm for multi-objective fuzzy optimization of Whey Fermentation

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CHEMICAL AND BIOCHEMICAL ENGINEERING QUARTERLY 2009年第2期23卷 153-159页

作者： Petrov, M. Ilkova, T. Bulgarian Acad Sci Ctr Biomed Engn BU-1113 Sofia Bulgaria

In this paper, multi-objective fuzzy optimization is applied to finding an optimal policy of a fed-batch fermentation process for lactose oxidation from a natural substratum by a strain Kluyveromyces marxianus var. laclis MC 5. The optimal policy consisted of feed flow rate, feed concentration of the substrate and initial concentration of the substrate. The multi-objective problem corresponds to the process productiveness, and the cost of the substrate. A simple combined algorithm guidelines the finding of a satisfactory solution to the general multi-objective optimization problem. The combined algorithm includes a method for random search for finding an initial point and a method based on fuzzy sets theory, combined in order to find the best solution of the optimization problem. The obtained optimal control results have shown an increase of process productiveness and a decrease of the remaining objective functions.

关键词： multi-objective fuzzy optimization fuzzy sets theory random search with back step combined algorithm whey fermentation

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Generic Technology Evaluation Method Based on multi-objective fuzzy optimization

Generic Technology Evaluation Method Based on Multi-objectiv...

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2nd International Conference on Information Management, Innovation Management and Industrial Engineering

作者： Tian, Xiaoyi Tang, Qinglong Wang, Ruizhi Univ Sci & Technol China Dept Management Hefei 230026 Peoples R China Univ Sci & Technol China Hefei 230026 Peoples R China Anhui Ankai Automobile Co Ltd Hefei Peoples R China

ISBN: (纸本)9780769538761

Generic technology, which can be widely applied to various industries, is of great economic and social benefits because of its fundamentality, externality and sharable nature. The present researches on generic technology are mainly directed by governments and generic technology projects always last long and cost a lot, so it is essential to do some studies on the evaluation system of generic technology. Based on a thorough analysis about the character of generic technology, an four factors evaluation model, which contains such aspects as competitiveness of technology, capacity of industrialization, capability of creating benefits and capacity of technology organizations, is proposed in this paper. Then, we build an evaluation index system based on the four factors model and obtain an optimal sequence selection of generic technology projects using multi-objective fuzzy optimization model. We hope that what we have done will be a feasible reference for the government generic technology selection.

关键词： generic technology evaluation index system multi-objective fuzzy optimization

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