The multivariable inverse artificial neural network combined with GA and PSO to improve the performance of solar parabolic trough collector

Ajbar, Wassila; Parrales, A.; Cruz-Jacobo, U.; Conde-Gutiérrez, R.A.; Bassam, A.; Jaramillo, O.A.; Hernández, J.A.

doi:10.1016/j.applthermaleng.2021.116651

Cited by 52 publications

(9 citation statements)

References 58 publications

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“…It is well reported in several studies that TANSIG–PURELIN combination demonstrated better adaptation to the ANN. 22,26,27 Demonstration of the ANN prediction is done in Table 2. The actual activity values are converted to normalized value to get a better standard comparison.…”

Section: Resultsmentioning

confidence: 99%

Artificial neural network-based modelling of optimized experimental study of xylanase production by Penicillium citrinum xym2

Kumar

Saha

Ghosh

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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The industrial production of enzymes is generally optimized by one-factor-at-a-time (OFAT) approach. However, enzyme production by the method involves submerged or solid-state fermentation, which is laborious and time-consuming and it does not consider interactions among process variables. Artificial neural network (ANN) offers enormous potential for modelling biochemical processes and it allows rational prediction of process variables of enzyme production. In the present work, ANN has been used to predict the experimental values of xylanase production optimized by OFAT. This makes the reported ANN model to predict further optimal values for different input conditions. Both single hidden layered (6-3-1) and double hidden layered (6-12-12-1) were able to closely predict the actual values with MSE equals to 0.004566 and 0.002156, respectively. The study also uses multiple linear regression (MLR) analysis to calculate and compare the outcome with ANN predicted xylanase activity, and to establish a parametric sensitivity.

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Section: Resultsmentioning

confidence: 99%

Artificial neural network-based modelling of optimized experimental study of xylanase production by Penicillium citrinum xym2

Kumar

Saha

Ghosh

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…Mathematical models based on artificial neural networks have been successfully used to solve various problems. Examples of successful use of artificial neural networks are: the development of a model for predicting the reliability of complex software systems [ 18 ]; the use of ANN to solve environmental problems [ 19 ]; the recognition of objects by the structure of a material [ 20 ]; the identification and diagnostics of technical objects [ 21 ]; the control of the technological process of thermochemical dehydration [ 22 ]; the management of an energy converter [ 23 ]; the control of the parameters of technological processes at thermal power plants [ 24 ]; the use of ANN to solve energy problems [ 25 , 26 ]. …”

Section: Methodsmentioning

confidence: 99%

The Use of Collections of Artificial Neural Networks to Improve the Control Quality of the Induction Soldering Process

Milov

Tynchenko

Kurashkin

et al. 2021

Sensors

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In industries that implement the technology of induction soldering, various sensors, including non-contact pyrometric ones, are widely used to control the technological process. The use of this type of sensor implies the need to choose a solution that is effective in different operating conditions in terms of the accuracy of the data obtained and the reliability of the measurement equipment and duplication in case of a failure. The present article discusses the development of intelligent technology based on a collection of artificial neural networks, which allows a number of problems associated with technological process control when using pyrometric sensors to be solved: assessing the quality of measurements, correcting measurements when non-standard errors are detected, and controlling the process of induction heating in the absence of reliable readings of the measurement instruments. The collection of artificial neural networks is self-configuring with the use of multicriterion genetic algorithms. The use of the proposed intelligent technology made it possible to improve the control quality of the technological process of the induction brazing of waveguide paths of spacecraft: the overregulation was decreased from 0–20 to 0, and the difference in the heating temperatures of the elements of the brazed waveguide assembly was decreased from 20–100 to 0–10. In addition, the overall process duration decreased and became more stable. When using the classical control technology, the time varied in the range of 20–60 s; when using the proposed technology, it stabilized in the range of 30–35 s.

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“…In order to identify the fan voltages that guarantee optimal forced convection conditions to improve the operation of the solar system and achieve the maximum drying velocity, a computational strategy known as ANNi [39,40] was implemented, which is based on the algebraic reduction of Equations ( 3) and ( 4). This approach uses the coefficients of the weights and bias of the ANN obtained after their training (W o , W i , b 1 , and b 2 ) and inverts the ANN to form the objective function defined by Equation (5).…”

Section: Optimization Approach By Inverse Annmentioning

confidence: 99%

“…In relation to results with this same approach reported in the literature, Cervantes-Bobadilla, et al [42] reported acceptable RMSE values of 0.4052 and 2.9178, while, in this work, the corresponding values were 0.1992 and 0.2139. Furthermore, Ajbar, et al [40] performed modeling with a similar ANNi approach, obtaining R values of 0.9753 and 0.9678, which were considered acceptable to proceed with the optimization phase. Compared to this, this study presented better correlation values (R of 0.9822 and 0.9757).…”

Section: Artificial Neural Network Modelmentioning

confidence: 99%

Experimental Evaluation and Theoretical Optimization of an Indirect Solar Dryer with Forced Ventilation under Tropical Climate by an Inverse Artificial Neural Network

et al. 2021

Self Cite

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In this theoretical–experimental study is presented a hybridization strategy based on the application of an inverse artificial neural network model (ANNi) coupled with metaheuristic optimization algorithms to optimize the drying velocity (vd) of an active indirect solar dryer for plantain and taro (Colocasia antiquorum). In the experimental stage, both fruits were evaluated in periods from 9:00 a.m. to 5:00 p.m. under a humid tropical climate region, varying the voltage of the air extractor fan (at 6 V, 9 V, and 12 V) to control the fan velocity. The experimental results showed that the maximum drying velocities were reached at 9 V, achieving a drying velocity of 1.5, 0.9, and 0.55 g/min, with a total drying time of 465 min for the taro, and 1.46, 1.46, and 0.36 g/min, with a total drying time of 495 min, for the plantain. As a result of the drying curves, it was observed that the drying velocity is higher in taro than in plantain. Subsequently, an artificial neural network (ANN) architecture was trained using the database generated from the solar dryer’s experimental stage. Six environmental variables and one operational variable were considered as the model’s inputs, feeding the ANN to estimate the drying velocity (vd), obtaining a linear regression coefficient R = 0.9822 between the experimental and simulated data. A sensitivity analysis was performed to determine the impact of all the input variables. A hybrid strategy based on ANNi was developed and evaluated with three metaheuristic optimization algorithms; the best result was obtained by genetic algorithms (ANNi-GA) with an error percentage of 0.83% and an average computational time of 4.3 s. The scope of this optimization strategy was to obtain a theoretical result that allows predicting the behavior of the dryer and improving its performance for its practical application in future work through the implementation in development boards. Lastly, the optimization strategy presented is not limited to indirect solar dryers and opens a research window for evaluating other solar drying technologies.

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The multivariable inverse artificial neural network combined with GA and PSO to improve the performance of solar parabolic trough collector

Cited by 52 publications

References 58 publications

Artificial neural network-based modelling of optimized experimental study of xylanase production by Penicillium citrinum xym2

Artificial neural network-based modelling of optimized experimental study of xylanase production by Penicillium citrinum xym2

The Use of Collections of Artificial Neural Networks to Improve the Control Quality of the Induction Soldering Process

Experimental Evaluation and Theoretical Optimization of an Indirect Solar Dryer with Forced Ventilation under Tropical Climate by an Inverse Artificial Neural Network

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