New procedure in solar system dynamic simulation, thermodynamic analysis, and multi-objective optimization of a post-combustion carbon dioxide capture coal-fired power plant

Mofidipour, Ehsan; Babaelahi, Mojtaba

doi:10.1016/j.enconman.2020.113321

Cited by 29 publications

(7 citation statements)

References 31 publications

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“…Therefore, its evolution direction is random, uncontrollable, and memoryless. Although real coding solves the problem of accuracy and storage capacity of the genetic algorithm to a great extent, its arithmetic crossover as the main crossover operator is a convex operation [12], so the whole group can only find the optimal value, may inexpertly converge to the local optimal value, and does not solve the lack of memory and randomness of the evolution direction [13]. The main problems of the coal-blending control system in the coal preparation plant are low automation and poor safety and reliability, which are difficult for meeting the needs of coal-blending control.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Optimization Analysis of Power Coal‐Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

Tao

Song²,

Zhu

et al. 2023

Journal of Sensors

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Research and optimization of the coal-blending system can greatly improve the variety diversity and quality stability of finished coal. However, the existing research on the coal-blending system has some defects, such as fuzzy ash content of coal-blending warehouse products and excessive error of the coal-blending ratio determined by artificial experience, which brings many disadvantages and difficulties to the efficient and stable production of the coal preparation plant. The genetic algorithm and intelligent sensor network are relatively advanced technologies at present. Therefore, the optimization analysis of the coal-blending model and its control system based on the intelligent sensor network and genetic algorithm has become a research hotspot. This paper first introduces the basic theory of the genetic algorithm and intelligent sensor network and their application in coal-blending research, then establishes an optimized dynamic coal-blending model based on the intelligent sensor network and genetic algorithm, and analyzes its application effect. The research results show that the coal quality prediction model can be mined from coal quality data and coal-blending data by using the genetic algorithm and ideas, and the monitoring system based on the intelligent sensor network can monitor the abnormal state anytime and anywhere. Compared with the traditional coal-blending method, the average error is 3.33%, and the accuracy is improved by 4.82%.

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Section: Introductionmentioning

confidence: 99%

[Retracted] Optimization Analysis of Power Coal‐Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

Tao

Song²,

Zhu

et al. 2023

Journal of Sensors

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“…Sliding mode control is a robust control approach that utilizes a discontinuous control approach to change the non-linear system dynamics and forces the system to slide around systems' normal behavior. For more information, see the reference [25].…”

Section: Problem Definition and Methodsmentioning

confidence: 99%

Thermodynamic Analysis, Advanced Non-Linear Dynamic Simulation and Multi-Criteria Optimization of a 100 MW Parabolic Trough Solar Steam Power Plant

Mofidipour

Babaelahi

Arabkoohsar

2021

International Journal of Thermodynamics

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Thermodynamic analyses of concentrating solar power plants and optimizing these cycles relying on the energetic and exergetic optimal efficiencies are quite common. However, it is found that just considering the efficiency of the system for performance optimization and neglecting the adverse effects of the plant's wrong operation timings, which is a function of the solar working fluid flow rate, can easily give misleading optimization results. This study's primary goal is to find the optimal operating point of the cycle in terms of the thermodynamic efficiencies considering the system's operating time. Thus, the Sliding Mode Control (SMC) approach is employed to provide a proper mass flow rate for the working fluid from the solar collector field to achieve a precise output temperature from the collectors in different operating conditions. Multi-objective optimization is performed using the Particle Swarm Optimization (PSO) algorithm. The results of power block sensitivity analysis indicate that a 70℃ increase in the solar collector outlet temperature remarkably enhances electricity generation and exergetic efficiency by 67 and 48%. The two-objective optimization shows 22.01%, 2.10%, and 5.46% enhancement in response time, thermal efficiency, and exergetic efficiency, and the three-objective optimization reveals 3.68% and 3.74% improvement in efficiency (thermal and exergetic), respectively.

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“…Overall price electricity = w op * price electricity , (20) in which, w op is a weight vector in which each element has maximum value of 1 and price is the vector with the electricity prices ($/kWh). In this case, the w op = [1.00 0.95 0.90 0.90 0.85 0.85 0.80 0.80 0.75 0.75], as the maximum simulated optimization horizon is 10 h. Moreover, the implementation horizon is 1 h based on the time scale that the forcing functions usually change.…”

Section: Optimization Formulation and Set Upmentioning

confidence: 99%

Nonlinear multiobjective and dynamic real‐time predictive optimization for optimal operation of baseload power plants under variable renewable energy

Kim

Lima

2022

Optim Control Appl Methods

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Considering the increase of disruptive variable renewable energy penetration into the power grid, this article focuses on the investigation of a multiobjective and dynamic real‐time optimization framework to address the cycling of large‐scale power plants under renewable penetration. In this framework, a parallelized particle swarm optimization step is first performed to generate feasible initial points. Then, a multiobjective and dynamic real‐time optimization formulation generates optimal trajectories. The benefit of predictive capability is investigated for the dynamic component, which introduces the novel nonlinear multiobjective and dynamic real‐time predictive optimization approach. Two multiobjective formulations to obtain Pareto front optimal in real time are explored: the modified Tchebycheff‐based weighted metric and ε‐constraint methods. Economic and environmental objectives are considered in this study. A novel topical discussion on the intersection of dynamic real‐time optimization with model predictive control is also presented. The developed framework is successfully applied to a baseload coal‐fired power plant with postcombustion CO2 capture. Results indicate that the approach can be deployed for a large‐scale system if automatic differentiation, model reduction, and parallelization are adopted to improve computational tractability, with computational improvement up to 120‐folds after performing these steps. Finally, market and carbon policies showed an impact on the optimal compromise between the objectives with an additional 63 ton of CO2 captured under favorable market conditions.

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New procedure in solar system dynamic simulation, thermodynamic analysis, and multi-objective optimization of a post-combustion carbon dioxide capture coal-fired power plant

Cited by 29 publications

References 31 publications

[Retracted] Optimization Analysis of Power Coal‐Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

[Retracted] Optimization Analysis of Power Coal‐Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm

Thermodynamic Analysis, Advanced Non-Linear Dynamic Simulation and Multi-Criteria Optimization of a 100 MW Parabolic Trough Solar Steam Power Plant

Nonlinear multiobjective and dynamic real‐time predictive optimization for optimal operation of baseload power plants under variable renewable energy

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