Global optimization algorithms applied to solve a multi-variable inverse artificial neural network to improve the performance of an absorption heat transformer with energy recycling

Solís-Pérez, J.E.; Gómez‐Aguilar, J.F.; Hernández, J.A.; Escobar-Jiménez, R.F.; Viera-Martin, E.; Conde-Gutiérrez, R.A.; Cruz-Jacobo, U.

doi:10.1016/j.asoc.2019.105801

Cited by 15 publications

(7 citation statements)

References 29 publications

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“…To perform the optimization, the multivariate objective function was constructed (based on the approach proposed by Solís-Pérez et al (2019)) using the weighting and bias coefficients calculated for F ABS , contained in Table 4: where X represents each operational parameter to be optimized, selected based on the sensitivity analysis’ relevance results for F ABS ( X 1 = m ˙ N H 3, X 2 = m ˙ D S, and X 3 = x DS ); whose inequality restrictions are given by: On the other hand, the values of P1 to P10 correspond to the sum of the non-optimizable inputs multiplied with their respective iw , which are considered as constants for the multivariate objective function: For the current study, the meta-heuristic algorithm selected was the Water Cycle Algorithm (WCA). It is a nature-inspired technique, proposed in 2012 by Eskandar et al (Eskandar et al, 2012), based on the geographic forming streams and rivers from the hydrological cycle.…”

Section: Multivariate Optimization Of Absorption Flux By Annimentioning

confidence: 99%

“…In the second stage, optimization algorithms, mainly heuristics, are applied to perform the inverse process and obtain the operational parameters that guarantee the desired value. This approach has been successfully used to improve the performance of thermal processes such as heat pumps (Conde-Gutiérrez et al, 2018; Solís-Pérez et al, 2019) and industrial solar heat systems (Ajbar et al, 2021; May Tzuc et al, 2017; Moheno-Barrueta et al, 2021); Nevertheless, according to the review, there are no reports of its use to improve the performance of absorption refrigeration systems.…”

Section: Introductionmentioning

confidence: 99%

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Multivariate inverse artificial neural network to analyze and improve the mass transfer of ammonia in a Plate Heat Exchanger-Type Absorber with NH₃/H₂O for solar cooling applications

Tzuc

Chan

Castañeda-Robles

et al. 2022

Energy Exploration & Exploitation

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This work presents a numerical approach to compute optimal operating conditions that maximize the absorption flux into a heat exchanger designed for absorption refrigeration systems. Experimental data were obtained from a test circuit that operates in bubble absorption mode with an inner vapor distributor into a Plate Heat Exchanger-type (PHE-type) and interacts with ammonia vapor, NH3-H2O refrigerant, and cooling water. An artificial neural network (ANN) was trained to correlate the thermal properties of the solution and absorption flux in function of easily measurable parameters (concentrations, mass flows, and pressures of saturated and diluted solutions, flow and temperature of the ammonium vapor, environment temperature, and solution temperature). According to results, ANN is adequate to correlate the operational parameters and the transport phenomena inside the heat exchanger with a precision > 99%. ANN also quantitatively identified the ammonium vapor flow (43.1%), dilute solution flow (18.1%), and dilute solution concentration (13.1%) as the variables most importantly in influencing absorption flux optimization. Subsequently, a multivariable inverse artificial neural network was applied to improve the mass transfer into the PHE-type.It was identified that simultaneous optimization of the ammonia and dilute concentration flow rates improves the absorption flow performance by up to 96.3% under a worst-case scenario (ammonia flow rate<1.4 kg/min) and even 7.04% when even when operating near the amino vapor flow limit (ammonia flow rate>2.0 kg/min). Finally, it was confirmed that incorporating the diluted solution concentration into the optimization contributes to improving the performance of the absorption process 1%. Results obtained are relevant in the search to produce more competitive absorption cooling systems, demonstrating the feasibility of improving the performance of heat exchangers without structural modifications. The proposed methodology represents an interesting option to be implemented to improve performance in solar cooling systems.

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Section: Multivariate Optimization Of Absorption Flux By Annimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multivariate inverse artificial neural network to analyze and improve the mass transfer of ammonia in a Plate Heat Exchanger-Type Absorber with NH₃/H₂O for solar cooling applications

Tzuc

Chan

Castañeda-Robles

et al. 2022

Energy Exploration & Exploitation

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“…Khajeh et al shows ANN-PSO achieves high extraction molybdenum extraction from water using silver nanoparticles by determining optimal volumes of the extractant and complex, pH, and adsorption time [18]. There have been other successes for optimization in pharmaceuticals, extraction, and HVAC [19][20][21][22][23][24][25][26]. While this shows ANN-PSO is widely used to optimize process inputs, newer algorithms may offer further improvements.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process

2020

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One popular method for optimizing systems, referred to as ANN-PSO, uses an artificial neural network (ANN) to approximate the system and an optimization method like particle swarm optimization (PSO) to select inputs. However, with reinforcement learning developments, it is important to compare ANN-PSO to newer algorithms, like Proximal Policy Optimization (PPO). To investigate ANN-PSO’s and PPO’s performance and applicability, we compare their methodologies, apply them on steady-state economic optimization of a chemical process, and compare their results to a conventional first principles modeling with nonlinear programming (FP-NLP). Our results show that ANN-PSO and PPO achieve profits nearly as high as FP-NLP, but PPO achieves slightly higher profits compared to ANN-PSO. We also find PPO has the fastest computational times, 10 and 10,000 times faster than FP-NLP and ANN-PSO, respectively. However, PPO requires more training data than ANN-PSO to converge to an optimal policy. This case study suggests PPO has better performance as it achieves higher profits and faster online computational times. ANN-PSO shows better applicability with its capability to train on historical operational data and higher training efficiency.

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“…Control algorithms are widely used in the field of automatic control field, 36,37 PID algorithm is one of useful control algorithms. 38,39 It has been widely used in the active magnetic bearing control system and is usually used as the main controller of the system.…”

Section: Introductionmentioning

confidence: 99%

Research on control strategy of an active magnetorheological fluid bearing based on improved gray wolf optimization approach

Lai

Hua

Liu

et al. 2023

Journal of Low Frequency Noise, Vibration and Active Control

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In order to address the problems of insufficient load capacity and rotor vibration, an active fluid-film bearing lubricated with magnetorheological fluid (MRF) is proposed. First, the geometry of the MRF fluid-film bearing is designed and its intelligent lubrication mechanism is analyzed to clarify its advantages. In addition, mathematical model of MRF fluid-film bearing-rotor system is derived, and FEM model is utilized to obtain stiffness and damping coefficients to supplement mathematical model. Moreover, an improved gray wolf optimization (IGWO) algorithm is developed to tune the PID controller parameters. The validity of the proposed method is verified by numerical simulation. Furthermore, the simulation results show that with the increasing of current magnitude, the orbits of shaft center decrease. Under the presence of magnetic fields, the shaft center orbits of the MRF bearing can converge to a point, and therefore this bearing has ability to suppress rotor vibration. Finally, IGWO-PID controller has better response characteristics than GWO, PSO, and GA algorithms, and hence the IGWO algorithm can find the more appropriate PID controller parameters, that the validity of the improved algorithm is further proved. Therefore, the active bearing and its research findings provide new reference for MRF vibration control in the field of journal bearing lubrication.

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Global optimization algorithms applied to solve a multi-variable inverse artificial neural network to improve the performance of an absorption heat transformer with energy recycling

Cited by 15 publications

References 29 publications

Multivariate inverse artificial neural network to analyze and improve the mass transfer of ammonia in a Plate Heat Exchanger-Type Absorber with NH₃/H₂O for solar cooling applications

Multivariate inverse artificial neural network to analyze and improve the mass transfer of ammonia in a Plate Heat Exchanger-Type Absorber with NH₃/H₂O for solar cooling applications

Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process

Research on control strategy of an active magnetorheological fluid bearing based on improved gray wolf optimization approach

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Global optimization algorithms applied to solve a multi-variable inverse artificial neural network to improve the performance of an absorption heat transformer with energy recycling

Cited by 15 publications

References 29 publications

Multivariate inverse artificial neural network to analyze and improve the mass transfer of ammonia in a Plate Heat Exchanger-Type Absorber with NH3/H2O for solar cooling applications

Multivariate inverse artificial neural network to analyze and improve the mass transfer of ammonia in a Plate Heat Exchanger-Type Absorber with NH3/H2O for solar cooling applications

Comparing Reinforcement Learning Methods for Real-Time Optimization of a Chemical Process

Research on control strategy of an active magnetorheological fluid bearing based on improved gray wolf optimization approach

Contact Info

Product

Resources

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Multivariate inverse artificial neural network to analyze and improve the mass transfer of ammonia in a Plate Heat Exchanger-Type Absorber with NH₃/H₂O for solar cooling applications

Multivariate inverse artificial neural network to analyze and improve the mass transfer of ammonia in a Plate Heat Exchanger-Type Absorber with NH₃/H₂O for solar cooling applications