The techno-economic and environmental analysis of genetic algorithm (GA) optimized cold thermal energy storage (CTES) for air-conditioning applications

Barthwal, Mohit; Dhar, Atul; Powar, Satvasheel

doi:10.1016/j.apenergy.2020.116253

Cited by 52 publications

(9 citation statements)

References 32 publications

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“…The schematic diagram of the parameter optimization process of time‐delay feedback control based on a genetic algorithm is shown in Figure . [ 34,35 ] The specific steps are as follows: The genetic algorithm generates an initial population. Each individual in the population is assigned to the delay feedback control parameter g in turn, τ ; the optimal control feedback force is obtained from Equation (2) and applied to the quarter car model. Finally, the performance index of the suspension is obtained. The fitness function value of each individual in the population is obtained from Equation (28) to judge whether it meets the termination condition of the genetic algorithm.…”

Section: Methodsmentioning

confidence: 99%

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Theoretical and Experimental Research on Active Suspension System with Time‐Delay Control

Wu,

Ren,

et al. 2023

Advcd Theory and Sims

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Time‐delay significantly impacts the control system's performance and even leads to the unexpected instability of the control system. This paper aims at the adverse effect of time‐delay on the control effect and stability of the active suspension system. This paper proposes to suppress vehicle body vibration by applying time‐delay displacement feedback. In this paper, the time‐delay control characteristics of the system are studied by combining theory with experiment. This paper first establishes the suspension system's dynamic Equation with time‐delay. Then, the frequency domain sweep method is used to analyze the stability of the control system. Finally, the time‐delay simulation model is established, and the single‐frequency excitation is used as the external excitation to compare with passive and backstepping control. The simulation results verify the superiority of the time‐delay control under external excitation. In this paper, through the 1/4 model bench test of the active suspension system, the simulation results and the test results under the same working conditions are compared, and it is found that the two have good consistency. The results show that the research is credible and can provide a reference for the actual design and application of active vehicle suspension controllers.

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Section: Methodsmentioning

confidence: 99%

“…The schematic diagram of the parameter optimization process of time-delay feedback control based on a genetic algorithm is shown in Figure 3. [34,35] The specific steps are as follows: 1) The genetic algorithm generates an initial population.…”

Section: Objective Functionmentioning

confidence: 99%

Theoretical and Experimental Research on Active Suspension System with Time‐Delay Control

Wu,

Ren,

et al. 2023

Advcd Theory and Sims

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“…Gas can effectively solve multiple types of optimization problems and deal with discontinuous objective functions by evaluating the fitness of the population. 38,49 Figure 3 shows a flowchart of the GA optimization. The data communication between the MATLAB software and Aspen HYSYS is established based on the Active X plug-in.…”

Section: Genetic Algorithm Optimization Modelmentioning

confidence: 99%

“…Gas can effectively solve multiple types of optimization problems and deal with discontinuous objective functions by evaluating the fitness of the population 38,49 . Figure 3 shows a flowchart of the GA optimization.…”

Section: System Descriptionmentioning

confidence: 99%

Optimization of a cryogenic liquid air energy storage system and its optimal thermodynamic performance

Tan

Wen

Ding

et al. 2022

Intl J of Energy Research

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Summary For grid‐scale intermittent electricity storage, liquid air energy storage (LAES) is considered to be one of the most promising technologies for storing renewable energy. In this study, a steady‐state process model was developed for an LAES, by combining a Linde liquefaction process and an open Rankine power cycle. To investigate the system performance and achieve global optimization, a single‐factor analysis approach and multifactor genetic algorithm (GA) optimization model were built using MATLAB software. The effects of the charging pressure, storage pressure, discharging pressure, and isentropic efficiency of the compressor/turbine on the LAES performance parameters, such as the inlet temperature of the Joule–Thomson valve, round‐trip efficiency (RTE), liquefaction ratio (LR), and power consumptions in the compressor, cryo‐pump, and turbine, were investigated. Subsequently, the optimal conditions were obtained using the GA optimization method to achieve the optimal performance of the LAES. The results showed that the charging pressure, discharging pressure, and isentropic efficiencies of the compressor and turbine had significant effects on the RTE; an increase in the discharging pressure resulted in an improved expansion power output; the GA optimization could achieve RTE, LR, the system energy storage and recovery exergy efficiencies of 53.33%, 86.96%, 81.00%, and 78.16%; the power consumption in the compressor of GA optimization was 10.02% maximum saving. The proposed optimization method can be used to further explore the global optimization of cryogenic energy storage systems, such as different‐layout LAES systems and different cryogenic liquefaction media energy storage systems.

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“…Mathematical methods can describe the main information flow of the LSTM hidden layer cell unit (Figure 1) (Barthwal et al, 2020). In Figure 1, x t , c t , and h t are the input unit, cell state, and output unit at time t, respectively; c t-1 and h t-1 are the cell state and output unit at time t-1, respectively; σ is the softmax function; ⊗ represents multiplication in the model; and the arrow represents the direction of information flow.…”

Section: Research Basis Long Short-term Memorymentioning

confidence: 99%

Chiller Fault Diagnosis Based on Automatic Machine Learning

Tian

Wang

et al. 2021

Front. Energy Res.

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Intelligent diagnosis is an important means of ensuring the safe and stable operation of chillers driven by big data. To address the problems of input feature redundancy in intelligent diagnosis and reliance on human intervention in the selection of model parameters, a chiller fault diagnosis method was developed in this study based on automatic machine learning. Firstly, the improved max-relevance and min-redundancy algorithm was used to extract important feature information effectively and automatically from the training data. Then, the long short-term memory (LSTM) model was used to mine the temporal correlation between data, and the genetic algorithm was employed to train and optimize the model to obtain the optimal neural network architecture and hyperparameter configuration. Finally, a transient co-simulation platform for building chillers based on MATLAB as well as the Engineering Equation Solver was built, and the effectiveness of the proposed method was verified using a dynamic simulation dataset. The experimental results showed that, compared with traditional machine learning methods such as the recurrent neural network, back propagation neural network, and support vector machine methods, the proposed automatic machine learning algorithm based on LSTM provides significant performance improvement in cases of low fault severity and complex faults, verifying the effectiveness and superiority of this method.

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The techno-economic and environmental analysis of genetic algorithm (GA) optimized cold thermal energy storage (CTES) for air-conditioning applications

Cited by 52 publications

References 32 publications

Theoretical and Experimental Research on Active Suspension System with Time‐Delay Control

Theoretical and Experimental Research on Active Suspension System with Time‐Delay Control

Optimization of a cryogenic liquid air energy storage system and its optimal thermodynamic performance

Chiller Fault Diagnosis Based on Automatic Machine Learning

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