Artificial Neural Network Simulation Research Articles

Dimension Prediction and Microstructure Study of Wire Arc Additive Manufactured 316L Stainless Steel Based on Artificial Neural Network and Finite Element Simulation

The dimensional accuracy and microstructure affect the service performance of parts fabricated by wire arc additive manufacturing (WAAM). Regulating the geometry and microstructure of such parts presents a challenge. The coupling method of an artificial neural network and finite element (FE) is proposed in this research for this purpose. Back-propagating neural networks (BPNN) based on optimization algorithms were established to predict the bead width (BW) and height (BH) of the deposited layers. Then, the bead geometry was modeled based on the predicted dimension, and 3D FE heat transfer simulation was performed to investigate the evolution of temperature and microstructure. The results showed that the errors in BW and BH were less than 6%, and the beetle antenna search BPNN model had the highest prediction accuracy compared to the other models. The simulated melt pool error was less than 5% with the experimental results. The decrease in the ratio of the temperature gradient and solidification rate induced the transition of solidified grains from cellular crystals to columnar dendrites and then to equiaxed dendrites. Accelerating the cooling rate increased the primary dendrite arm spacing and δ-ferrite content. These results indicate that the coupling model provides a pathway for regulating the dimensions and microstructures of manufactured parts.

Granular Magnetization Switching in Pt/Co/Ti Structure with HfOx Insertion for In-Memory Computing Applications.

Exploring multiple states based on the domain wall (DW) position has garnered increased attention for in-memory computing applications, particularly focusing on the utilization of spin-orbit torque (SOT) to drive DW motion. However, devices relying on the DW position require efficient DW pinning. Here, we achieve granular magnetization switching by incorporating an HfOx insertion layer between the Co/Ti interface. This corresponds to a transition in the switching model from the DW motion to DW nucleation. Compared to the conventional Pt/Co/Ti structure, incorporation of the HfOx layer results in an enhanced SOT efficiency and a lower switching current density. We also realized stable multistate storage and synaptic plasticity by applying pulse current in the Pt/Co/HfOx/Ti device. The simulation of artificial neural networks (ANN) based on the device can perform digital recognition tasks with an accuracy rate of 91%. These results identify that DW nucleation with a Pt/Co/HfOx/Ti based device has potential applications in multistate storage and ANN.

Electroosmotic microchannel flow of blood conveying copper and cupric nanoparticles: Ciliary motion experiencing entropy generation using backpropagated networks

AbstractA novel mathematical model for a hybrid (Cu–CuO/blood) Jeffrey nanofluid passing a vertical symmetric microchannel along with an electroosmosis pump is presented. The focuses on the advancement of mathematical modeling techniques, its comprehensive analysis of microfluidic system dynamics, and its potential to inform the optimal design of devices using nanofluids with broad applications in various fields. Arrhenius's law is used to analyze endothermic–exothermic reactions and activation energy. The governing partial differential equations of the fixed frame are transformed into ordinary differential equations of the wave frame using self‐similarity transformations. Low Reynolds number and long‐wavelength approximations helped to find solutions of the equations by applying a suitable BVP solver in MATLAB. The fluid's velocity, temperature, concentration, and electroosmosis properties are studied graphically. Two‐dimensional contour plots of fluid velocity and three‐dimensional surface plots of fluid properties are discussed. Physically significant quantities of mass transfer rate, skin friction coefficient, entropy generation, and heat transfer rate are studied using contour plots. Artificial neural network simulation using Bayesian regularization backpropagation algorithms is analyzed for training state, error histogram, fit, performance, and regression plots. Conclusively, the comprehensive analysis of the fluid dynamics, entropy generation, mass and heat transfer, and in the microchannel, coupled with the successful implementation of artificial neural network simulation, contributes to an improved understanding of the system's behavior. Entropy generation was raised for enhanced Brownian motion number and reduced values of thermophoresis, activation energy, and endothermic–exothermic reaction parameters. This study's results can be used to improve the efficiency and effectiveness of microfluidic devices used in fields as diverse as electrical cooling and medicine delivery.

Characteristics analysis and structure optimization of a hybrid micro-jet impingement/micro-channel heat sink

To solve the heat dissipation problem of high-power electronics, a novel hybrid micro-jet impingement/micro-channel heat sink is proposed in this work. Different from the continuous and straight rectangular micro-channel, the inclined and interrupted channel walls (fins) are adopted to enhance the fluid disturbance inside the heat sink. To obtain the thermal–hydraulic performance of the hybrid heat sink, an overall 3D modeling and numerical simulations are conducted instead of local cell numerical simulation. In addition, to further improve the heat transfer and flow resistance performance, the effects of variation of structural parameters on heat transfer and flow resistance properties are investigated, including the fin inclination angle, fin length and fin height. The results of structural parametric analysis reveal that there are interactions between different structural parameters, but with different levels of significance for different evaluation indicators. Then, to obtain the optimal combinations of structural parameters for different applications, the multi-objective genetic algorithm coupled with artificial neural network (ANN) is utilized to search for the optimal value of each structural parameter. The heat transfer thermal resistance, pressure drop and mean absolute temperature deviation of the heated surface are utilized as the optimization objectives to evaluate the cooling and resistance characteristics. The optimization results obtained by three-objective optimization and two-objective optimization are analyzed and compared. Finally, seven optimal compromise solutions are selected from the Pareto front by using TOPSIS algorithm with different weight factor matrices. For the optimal compromise solutions, the maximum relative errors between the predicted values of the ANN and numerical simulation values are only 3%, which further demonstrates the accuracy of the artificial neural network model and the effectiveness of using multi-objective optimization to guide heat sink design.

Enhancement of the Synaptic Performance of Phosphorus-Enriched, Electric Double-Layer, Thin-Film Transistors

The primary objective of neuromorphic electronic devices is the implementation of neural networks that replicate the memory and learning functions of biological synapses. To exploit the advantages of electrolyte gate synaptic transistors operating like biological synapses, we engineered electric double-layer transistors (EDLTs) using phosphorus-doped silicate glass (PSG). To investigate the effects of phosphorus on the EDL and synaptic behavior, undoped silicate spin-on-glass-based transistors were fabricated as a control group. Initially, we measured the frequency-dependent capacitance and double-sweep transfer curves for the metal-oxide-semiconductor (MOS) capacitors and MOS field-effect transistors. Subsequently, we analyzed the excitatory post-synaptic currents (EPSCs), including pre-synaptic single spikes, double spikes, and frequency variations. The capacitance and hysteresis window characteristics of the PSG for synaptic operations were verified. To assess the specific synaptic operational characteristics of PSG-EDLTs, we examined EPSCs based on the spike number and established synaptic weights in potentiation and depression (P/D) in relation to pre-synaptic variables. Normalizing the P/D results, we extracted the parameter values for the nonlinearity factor, asymmetric ratio, and dynamic range based on the pre-synaptic variables, revealing the trade-off relationships among them. Finally, based on artificial neural network simulations, we verified the high-recognition rate of PSG-EDLTs for handwritten digits. These results suggest that phosphorus-based EDLTs are beneficial for implementing high-performance artificial synaptic hardware.

Application of Neural Network Models with Ultra-Small Samples to Optimize the Ultrasonic Consolidation Parameters for 'PEI Adherend/Prepreg (CF-PEI Fabric)/PEI Adherend' Lap Joints.

The aim of this study was to optimize the ultrasonic consolidation (USC) parameters for 'PEI adherend/Prepreg (CF-PEI fabric)/PEI adherend' lap joints. For this purpose, artificial neural network (ANN) simulation was carried out. Two ANNs were trained using an ultra-small data sample, which did not provide acceptable predictive accuracy for the applied simulation methods. To solve this issue, it was proposed to artificially increase the learning sample by including additional data synthesized according to the knowledge and experience of experts. As a result, a relationship between the USC parameters and the functional characteristics of the lap joints was determined. The results of ANN simulation were successfully verified; the developed USC procedures were able to form a laminate with an even regular structure characterized by a minimum number of discontinuities and minimal damage to the consolidated components.

An artificial intelligence-based model for optimal conjunctive operation of surface and groundwater resources

A hybrid simulation-optimization model is proposed for the optimal conjunctive operation of surface and groundwater resources. This second-level model is created by finding and combining the best aspects of two resilient metaheuristics, the moth swarm algorithm and the symbiotic organization search algorithm, and then connecting the resulting algorithm to an artificial neural network simulator. For assessment of the developed model efficiency, its results are compared with two first-level simulation-optimization models. The comparisons reveal that the operation policies obtained by the developed second-level model can reliably supply more than 99% of the total demands in the study regions, indicating its superior efficiency compared to the two other first-level models. In addition, the highest sustainability index in the study regions belongs to the proposed model. Comparing the results of this research with those of other recent studies confirm the supremacy of the developed second-level model over several previously developed models.

Near‐Infrared‐Sensing Flexible Organic Synaptic Transistors with Water‐Processable Charge‐Trapping Polymers for Potential Neuromorphic Computing/Skin Applications

Neuromorphic devices, which can mimic the human body's neural system, are rising as an essential technology for artificial intelligence. Here, two types of organic synaptic transistors (OSTRs), OSTR‐A and OSTR‐B, are fabricated on either glass or polymer film using water‐processable charge‐trapping gate‐insulating layers that are prepared by reacting ethylenediamine (EDA) and poly(2‐acrylamido‐2‐methyl‐1‐propanesulfonic acid) (PAMPSA). OSTR‐A is designed to function as a basic artificial synapse by gate pulse stimulation only, while OSTR‐B has additional near‐infrared (NIR)‐absorbing conjugated polymer layers for further sensing of NIR light upon gate voltage stimulations. The PAMPSA:EDA films are found to contain permanent charge bridges (ion pairs of –SO3− +NH3‐) that play a charge‐trapping role in OSTRs. Both devices with the PAMPSA:EDA layers exhibit clear postsynaptic current (PSC) signals upon gate voltage pulses, leading to long‐term potentiation/depression characteristics. The flexible OSTR‐B devices can sense the NIR light (905 nm) upon gate pulse stimulation and their PSC signals are well maintained even after bending (>5000 times). Artificial neural network simulations disclose that the flexible OSTR‐B devices can stably perform synaptic operations under the NIR light with high accuracy (>90%) even after repeated bending (5000 times), indicative of potential use in artificial neuromorphic skin applications.

ARTIFICIAL NEURAL NETWORKS TO ENHANCE THE RING MACHINE EFFICIENCY AND YARN QUALITY BY DETERMINATION AND OPTIMIZATION OF DYNAMIC YARN TENSION

The yarn spinning process involves the interaction of large varieties of variables. The relation between the dynamic yarn tension (DYT), yarn quality, and production efficiency of the spinning frame cannot be established conclusively. Artificial neural network (ANN) is a promising step in this filed. In this research work, ANNs simulation and modeling is applied for the optimization of the DYT n to improve the production efficiency and quality of yarn. The research to date in DYT is insufficient to meet the developmental requirement of the high-speed and efficient ring spinning frame. One of the major problems facing the effective use of the ANN is the correct selection of the input parameters to be fed for the training of ANNs. Data of various input variables such as count, traveler no., spindle speed and dynamic yarn tension etc., was used for ANN modeling and simulation. DYT plays a significant role in the determination of yarn quality and its productivity in terms of end breakage rate. However, it has never been explained in terms of displacement from the original yarn path. This work is aimed to the determination and optimization of DYT at ring spinning frame. The influence of different yarn geometry parameters on DYT, measured by the tensiometer was investigated. The optimized DYT values for the machines, running at different speed and different counts were determined using ANN modeling. It is found that the optimized values predicted from ANN resulted in better quality, high production, and decreased end-breakage at industrial ring spinning frames. By the implementation of ANNs the optimum speed and effective utilization of textile raw materials can be achieved.

Versatile Neuromorphic Modulation and Biosensing based on N-type Small-molecule Organic Mixed Ionic-Electronic Conductors.

The ion/chemical-based modulation feature of organic mixed ionic-electronic conductors (OMIECs) are critical to advancing next generation bio-integrated neuromorphic hardware. Despite achievements with polymeric OMIECs in organic electrochemical neuronal synapse (OENS). However, small molecule OMIECs based OENS has not yet been realized. Here, for the first time, we demonstrate an effective materials design concept of combining n-type fused all-acceptor small molecule OMIECs with subtle side chain optimization that enables robustly and flexibly modulating versatile synaptic behavior and sensing neurotransmitter in solid or aqueous electrolyte, operating in accumulation modes. By judicious tuning the ending side chains, the linear oligoether and butyl chain derivative gNR-Bu exhibits higher recognition accuracy for a model artificial neural network (ANN) simulation, higher steady conductance states and more outstanding ambient stability, which is superior to the state-of-art n-type OMIECs based OENS. These superior artificial synapse characteristics of gNR-Bu can be attributed to its higher crystallinity with stronger ion bonding capacities. More impressively, we unprecedentedly realized n-type small-molecule OMIECs based OENS as a neuromorphic biosensor enabling to respond synaptic communication signals of dopamine even at sub-μM level in aqueous electrolyte. This work may open a new path of small-molecule ion-electron conductors for next-generation ANN and bioelectronics.

Versatile Neuromorphic Modulation and Biosensing based on N‐type Small‐molecule Organic Mixed Ionic‐Electronic Conductors

AbstractThe ion/chemical‐based modulation feature of organic mixed ionic‐electronic conductors (OMIECs) are critical to advancing next generation bio‐integrated neuromorphic hardware. Despite achievements with polymeric OMIECs in organic electrochemical neuronal synapse (OENS). However, small molecule OMIECs based OENS has not yet been realized. Here, for the first time, we demonstrate an effective materials design concept of combining n‐type fused all‐acceptor small molecule OMIECs with subtle side chain optimization that enables robustly and flexibly modulating versatile synaptic behavior and sensing neurotransmitter in solid or aqueous electrolyte, operating in accumulation modes. By judicious tuning the ending side chains, the linear oligoether and butyl chain derivative gNR‐Bu exhibits higher recognition accuracy for a model artificial neural network (ANN) simulation, higher steady conductance states and more outstanding ambient stability, which is superior to the state‐of‐art n‐type OMIECs based OENS. These superior artificial synapse characteristics of gNR‐Bu can be attributed to its higher crystallinity with stronger ion bonding capacities. More impressively, we unprecedentedly realized n‐type small‐molecule OMIECs based OENS as a neuromorphic biosensor enabling to respond synaptic communication signals of dopamine even at sub‐μM level in aqueous electrolyte. This work may open a new path of small‐molecule ion‐electron conductors for next‐generation ANN and bioelectronics.

Polarized Tunneling Transistor for Ultralow-Energy-Consumption Artificial Synapse toward Neuromorphic Computing.

Neural networks based on low-power artificial synapses can significantly reduce energy consumption, which is of great importance in today's era of artificial intelligence. Two-dimensional (2D) material-based floating-gate transistors (FGTs) have emerged as compelling candidates for simulating artificial synapses owing to their multilevel and nonvolatile data storage capabilities. However, the low erasing/programming speed of FGTs renders them unsuitable for low-energy-consumption artificial synapses, thereby limiting their potential in high-energy-efficient neuromorphic computing. Here, we introduce a FGT-inspired MoS2/Trap/PZT heterostructure-based polarized tunneling transistor (PTT) with a simple fabrication process and significantly enhanced erasing/programming speed. Distinct from the FGT, the PTT lacks a tunnel layer, leading to a marked improvement in its erasing/programming speed. The PTT's highest erasing/programming (operation) speed can reach ∼20 ns, which outperforms the performance of most FGTs based on 2D heterostructures. Furthermore, the PTT has been utilized as an artificial synapse, and its weight-update energy consumption can be as low as 0.0002 femtojoule (fJ), which benefits from the PTT's ultrahigh operation speed. Additionally, PTT-based artificial synapses have been employed in constructing artificial neural network simulations, achieving facial-recognition accuracy (95%). This groundbreaking work makes it possible for fabricating future high-energy-efficient neuromorphic transistors utilizing 2D materials.

Large scale integrated IGZO crossbar memristor array based artificial neural architecture for scalable in-memory computing

Neuromorphic systems based on memristor arrays have not only addressed the von Neumann bottleneck issue but have also enabled the development of computing applications with high accuracy. In this study, an artificial neural architecture based on a 10 × 10 IGZO memristor array is presented to emulate synaptic dynamics for performing artificial intelligence (AI) computing with high recognition accuracy rate. The large area 10 × 10 IGZO memristor array was fabricated using the photolithography method, resulting in stable and reliable memory operations. The bipolar switching at −2 V–2.5 V, endurance of 500 cycles, retention of >104 s, and uniform Vset/Vreset operation of 100 devices were achieved by modulating the oxygen vacancy in the IGZO film. The emulation of electric synaptic dynamics was also observed, including potentiation-depression, multilevel long-term memory (LTM), and multilevel short-term memory (STM), revealing highly linear and stable synaptic functions at different modulated pulse settings. Additionally, electrical modeling (HSPICE) with vector-matrix measurements and simulation of various artificial neural network (ANN) algorithms, such as convolution neural network (CNN) and spiking neural network (SNN), were performed, demonstrating a linear increase in current accumulation with high recognition rates of 99.33 % and 86.46 %, respectively. This work provides a novel approach for overcoming the von Neumann bottleneck issue and emulating synaptic dynamics in various neural networks with high accuracy.

Synaptic Characteristics of Atomic Layer-Deposited Ferroelectric Lanthanum-Doped HfO2 (La:HfO2) and TaN-Based Artificial Synapses.

Analog synaptic devices have made significant advances based on various electronic materials that can realize the biological synapse properties of neuromorphic computing. Ferroelectric (FE) HfO2-based materials with nonvolatile and low power consumption characteristics are being studied as promising materials for application to analog synaptic devices. The gradual reversal of FE multilevel polarization results in precise changes in the channel conductance and allows analogue synaptic weight updates. However, there have been few studies of FE synaptic devices doped with La, Y, and Gd. Furthermore, an investigation of interface quality is also crucial to enhance the remnant polarization (Pr), synaptic conductance linearity, and reliability characteristics. In this study, we demonstrate improved FE and artificial synaptic characteristics using an atomic layer-deposited (ALD) lanthanum-doped HfO2 (La:HfO2) and TaN electrode in the structure of an FE thin-film transistor (ITO/IGZO/La:HfO2/TaN), where indium-tin oxide (ITO) and indium-gallium-zinc oxide (IGZO) were used as source/drain and channel materials, respectively. Improved Pr and lower surface roughness were achieved by doped HfO2 and ALD TaN thin films. This synaptic transistor shows long-term potentiation and long-term depression with 200 levels of conductance states, high linearity (Ap, 0.97; Ad, 0.86), high Gmax/Gmin (∼6.1), and low cycle-to-cycle variability. In addition, a pattern recognition accuracy higher than 90% was achieved in an artificial neural network simulation.

Formulation and Comparison of Experimental based Mathematical Model with Artificial Neural Network Simulation on Surface Roughness with Burnished Spherical Surface Tool on Aluminum Alloy6351

This paper deals with the effect of burnishing process on the Aluminum Alloy material 6351(HE 15) using Lathe. Surface roughness generated after the turning operation was used to ball burnishing. The surface roughness pattern which was further used to simulate ball burnishing process using ANN. Improvement in the surface roughness values achieved for tool steel after ball burnishing process was 98.2429%.

Stretchable photosynaptic transistor with an ultralow energy consumption conferred using conjugated block copolymers/perovskite quantum dots nanocomposites

Emulation of photonic synapses offers a promising avenue for developing low-energy consumption of soft electronics, neurologically inspired robotics, and neuromorphic network computation. In this paper, a fully stretchable photosynaptic device with ultralow energy consumption using intrinsically stretchable poly(δ-decanolactone) (PDL)-based conjugated block copolymers (BCPs) with perovskite quantum dots (PeQDs) is first reported. The findings reveal that selectively choosing solvents for the PDL-based BCPs effectively regulates the assembly of P3HT and the accommodation of PeQDs, leading to improved self-aggregation of PeQDs, increased grain size, and optimized interfaces between P3HT and PeQDs The BCPs/PeQDs composite effectively emulates significant features of photonic synapses, such as paired-pulse facilitation (PPF), spike-dependent and short/long-term neuroplasticity, demonstrating excellent performance, including the fastest response time (1 ms), the highest current contrast (4.9 × 105), PPF (1.93) and ultra-low energy consumption (0.3 aJ) at an operating voltage of –0.1 mV. Furthermore, the BCP/PeQDs exhibit remarkable neuromuscular synapse characteristics, including high strain and bending tolerance and spike-dependent plasticity, enabling the devices to achieve high classification accuracy in artificial neural network simulations during tensile strain. The accommodation solvent selectivity of BCP/PeQDs suggests a promising strategy for advancing neurologically soft electronics, human-like pattern recognition, and neuromorphic computation.

Improvement of the classical artificial neural network simulation model of the parabolic trough solar collector outlet temperature and thermal efficiency using the conformable activation functions

The outlet temperature and the thermal efficiency predictions of the PTSC are essential parameters in the solar thermal power system. Therefore, it is crucial to have a prediction model that can predict its spatiotemporal behavior to the greatest extent possible. For that, the present study provides improved models of the classical ANN model by using ELU, swish, softplus, logsig, and tansig functions through the exponential transfer function to improve the outlet temperature prediction performance and thermal efficiency of PTSC. The PTSC’s outlet temperature simulation showed that the ANN model of topology 6-2-1 with the conformable swish transfer function (cswish) was the best model among nine other studied models, with an R2 and Radj2 of 0.9988, and MAE of 0.0052. The simulation of PTSC’s thermal efficiency proved that the conformable functions logsig, tansig, ELU and the classical ELU function involved in the ANN model for 6-3-1 achieved better results with approximately 95% precision. With the non-integer activation functions, was reduced a neuron in the hidden layer, which leads to the simplicity of the prediction model compared to the use of the classical activation functions. The proposed activation functions do not require high-performance features according to their execution times. Therefore, it could be a good alternative for engineers to apply in other thermal energy systems to make their prediction models more accessible and practical in the control and optimization process.

Development of data-driven models for the optimal design of multilayer sand filters for on-site treatment of greywater

Greywater, with limited content of pathogens, makes up more than half of the produced wastewater in urban areas. Given the high cost of wastewater management and treatment, it causes sense to collect greywater separately at the source and employ an on-site treatment system to increase opportunities for on-site water reuse. For this purpose, this paper aims to propose a multilayer granular filter as an inexpensive and simple on-site treatment method for greywater reuse. Furthermore, as determining the optimal structure of multilayer filters is a serious challenge, a simulation-optimization model is developed for determining the best filter configuration. An Artificial Neural Network (ANN) is trained based on experimental results to simulate the filter performance with different combinations of layers and the Genetic Algorithm (GA) is used to find the optimal thickness of different layers based on ANN simulation results. The proposed filter in this paper for greywater treatment consists of silica sand (in three different gradings) and activated carbon (with fixed grading) and treatment measures for evaluation of filter performance are considered as Chemical Oxygen Demand (COD) and Electrical Conductivity (EC). Due to difficulties in collecting, transferring, and storing the real greywater, synthetic greywater was used in this study. 49 experiments with different combinations of filter media thicknesses were performed and the performance of the filter was analyzed. Generally, three-layer filters perform better in COD and EC reduction, however, the average COD and EC elimination equals 36.3% and 15.1%, respectively, which indicates more efficiency of filter in COD reduction in comparison with EC. Based on the optimization-simulation model and experimental results, a filter consisting of 33 cm of fine sand, 20 cm of activated carbon, and 7 cm of medium sand results in the maximum efficiency and can reduce the COD and EC of greywater by 72% and 30%, simultaneously. According to the optimization outputs, the ideal filter can treat greywater up to having EC of 1000 μS/cm and COD of 321 mg/L, which is generally suitable for irrigation purposes.

Design Optimization of a Wafer Dough Blade Using Artificial Neural Network and Monte-Carlo Simulation

In this work, a systematic method to conduct the surrogate-based design optimization is proposed by utilizing Artificial Neural Network and Monte Carlo Simulation. To show its applicability, the design optimization of a wafer dough blade that is an important component in the food industry is carried out. In the optimization problem, design variables or inputs are totally six variables including distances, diameter and thickness, and design responses or outputs are the blade mass, the maximum stress occurred on it, and its surface area. When the results of the initial and optimum designs are compared, there is a significant decrease in the maximum stress (nearly 66%) whereas there was a reasonable low difference in both the mass and surface area. Thanks to the proposed method, it can be possible to take into account the experimental data instead of analytical data in a design problem. Moreover, the followed method provides engineers with a practical and systematic way to find the optimum solution for even nonlinear problems needs to be solved during engineering design process.

Artificial neural network simulation and development of a predictive model to anticipate performance of a hybrid plant combined with PVT solar system

This article uses the neural network method and numerical data for predicting the performance of an integrated energy system. The studied system is an integrated energy system consisting of a gas turbine with an air preheater and subsystems such as the Kalina cycle, supercritical carbon oxide cycle and hydrogen production. In this unit, a photovoltaic thermal (PVT) module has been used to generate electric power, as well as heating and starting the organic Rankine cycle. In addition, a Proton exchange membrane (PEM) electrolyzer to produce hydrogen is integrated. To propose a model that can predict the behavior of the integrated system by changing the parameters, the important outputs of the system such as energy efficiency, exergy efficiency, exergy destruction rate and net output power have been extracted in the form of functions according to the input parameters namely T11, P11, P18, Rp. The results show that the suggested functions of four outputs have the acceptable value for mean square error and regression coefficient which show high accuracy. The influence of each variable on each other and their interaction on the responses are drawn and adequately explained using three-dimensional response surface diagrams. The analysis represented that the amount of exergy destruction in the high-pressure ratio is at its maximum value with the increase in the temperature of point 4. In addition, it is at its maximum value in the high-pressure ratio with different changes in the pressure of point 11. Generally, the p-values for predicted values less than 0.001 represent that the model is statistically considerable at the 99 % confidence level. The p-values for suggested functions show a high level of confidence.