Persona: Golpour, Iman
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igolpour@ind.uned.es
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Golpour
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Iman
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Publicación Intelligence-based prediction of coefficient of performance for a novel high-temperature industrial heat pump: Comparative performance of ANN and ANFIS models(Elsevier, 2026-02-02) Golpour, Iman; Marcos del Cano, José Daniel; Barbero Fresno, Rubén; Rovira de Antonio, Antonio José; Butean, Alex; Høeg, Arne; Comisión EuropeaThis study presents a comparative evaluation of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) approaches for predicting the coefficient of performance (COP) of the HoegTemp, a high-temperature heat pump (HTHP) based on a Stirling cycle (SC) with a design heat capacity of 400 kW. Experimental tests were conducted at the IVAR biogas facility in Stavanger, Norway. This study employed a feedforward backpropagation neural network (FFBPNN) model with one and two hidden layers, with various numbers of neurons and three activation functions, as well as the ANFIS approach, to estimate the COP of the SC-HTHP. The FFBPNN model used the Levenberg-Marquardt (LM) and Bayesian regularization (BR) training algorithms, while the ANFIS model utilized a hybrid optimization method and grid partitioning. The ANN and ANFIS models were evaluated using the following input variables: temperature ratio (1.4–1.6 K/K), average source temperature (21–22 °C), average sink temperature (139–199 °C) and hot water inlet temperature (137–197 °C), with COP as the output variable. The results demonstrated that the FFBP-ANN model exhibited superior predictive accuracy compared to the ANFIS model, achieving R2 = 0.9999, MSE = 0.00010, MAE = 0.00804, and RMSE = 0.01000, whereas the ANFIS approach resulted in R2 = 0.9863, MSE = 0.00019, MAE = 0.01114, and RMSE = 0.01392. The optimal ANN topology was 4–23-16–1 with tansig–logsig–purelin activation functions. In contrast, the best membership functions selected for ANFIS were Gaussian for the input layer and constant for the output layer.Publicación Decarbonizing European industry: a novel technology to heat supply using waste and renewable energy(MDPI, 2024-10-06) Marcos del Cano, José Daniel; Golpour, Iman; Barbero Fresno, Rubén; Rovira de Antonio, Antonio José; Comisión EuropeaThis study examines the potential for the smart integration of waste and renewable energy sources to supply industrial heat at temperatures between 150 °C and 250 °C, aiming to decarbonize heat demand in European industry. This work is part of a European project (SUSHEAT) which focuses on developing a novel technology that integrates several innovative components: a Stirling cycle high-temperature heat pump (HTHP), a bio-inspired phase change material (PCM) thermal energy storage (TES) system, and a control and integration twin (CIT) system based on smart decision-making algorithms. The objective is to develop highly efficient industrial heat upgrading systems for industrial applications using renewable energy sources and waste heat recovery. To achieve this, the specific heat requirements of different European industries were analyzed. The findings indicate that industrial sectors such as food and beverages, plastics, desalination, textiles, ceramics, pulp and paper, wood products, canned food, agricultural products, mining, and chemicals, typically require process heat at temperatures below 250 °C under conditions well within the range of the SUSHEAT system. Moreover, two case studies, namely the Pelagia and Mandrekas companies, were conducted to validate the effectiveness of the system. An analysis of the annual European heat demand by sector and temperature demonstrated that the theoretical potential heat demand that could be met by the SUSHEAT system is 134.92 TWh annually. Furthermore, an environmental impact assessment estimated an annual significant reduction of 19.40 million tonnes of CO2 emissions. These findings underscore the significant potential of the SUSHEAT system to contribute to the decarbonization of European industry by efficiently meeting heat demand and substantially reducing carbon emissions.Publicación Exergetic sustainability assessment of a semi-industrial convective dryer employing waste heat recovery for drying wood chips: A BPANN-based approach(Elsevier, 2026-01-12) Zohrabi, Saman; Sadegh Seiiedlou, Seyed; Golpour, Iman; Mellmann, Jochen; Sturm,Barbara; Marcos del Cano, José Daniel; Blanco-Marigorta, Ana M.; Didaran, Fardad; Lefsrud, MarkThis study investigates the exergy-based performance and sustainability of a semi-industrial convective dryer equipped with a waste heat recovery unit for drying poplar wood chips. Four key exergy-related indicators, namely exergetic improvement potential (EIP), exergetic sustainability index (ESI), universal exergetic efficiency (UEE), and overall exergetic efficiency (OEE) were examined and then predicted using a feedforward backpropagation multilayer perceptron neural network (FFBP-MLPNN) with the Levenberg-Marquardt (LM) learning algorithm and a single hidden layer. The network evaluated different numbers of neurons in the hidden layer and utilized tansig and purelin activation functions in the hidden and output layers, respectively. Experimental trials demonstrated that increasing the air recirculation ratio enhances the ESI due to improved heat recovery and reduced exergy losses, while it reduces the EIP, indicating lower thermodynamic inefficiencies and less potential for further improvement. In contrast, lower recirculation ratios yielded lower ESI values and higher EIP, highlighting greater exergy destruction and larger optimization potential. Additionally, increased air temperatures and flow rates improved both indices. The results indicated that the neural network can predict all four outcomes with R2 > 0.97. Additionally, 0.013601 (using a 4–23–1 topology), 6.4137 × 10−6 (4–15–1 topology), 3.186 × 10−6 (4–30–1 topology), and 0.036108 (4–32–1 topology) were the mean squared error (MSE) values for predicting the EIP, ESI, UEE, and OEE, respectively. Hence, this study suggests that the ANNs approach could be an effective tool for analyzing thermal sustainability indicators in industrial convective drying processes.Publicación A Thermal Model for Rural Housing in Mexico: Towards the Construction of an Internal Temperature Assessment System Using Aerial Thermography(MDPI, 2024-09-26) Moctezuma-Sánchez, Miguel; Espinoza Gómez, David; López-Sosa, Luis Bernardo; Golpour, Iman; Morales-Máximo, Mario; González-Carabes, RicardoEstimating energy flows that affect temperature increases inside houses is crucial for optimizing building design and enhancing the comfort of living spaces. In this study, a thermal model has been developed to estimate the internal temperature of rural houses in Mexico using aerial thermography. The methodology used in this study considered three stages: (a) generating a semi-experimental thermal model of heat transfer through roofs for houses with high infiltration, (b) validating the model using contact thermometers in rural community houses, and (c) integrating the developed model using aerial thermography and Python 3.11.4 into user-friendly software. The results demonstrate that the thermal model is effective, as it was tested on two rural house configurations and achieved an error margin of less than 10% when predicting both maximum and minimum temperatures compared to actual measurements. The model consistently estimates the internal house temperatures using aerial thermography by measuring the roof temperatures. Experimental comparisons of internal temperatures in houses with concrete and asbestos roofs and the model’s projections showed deviations of less than 3 °C. The developed software for this purpose relies solely on the fundamental thermal properties of the roofing materials, along with the maximum roof temperature and ambient temperature, making it both efficient and user-friendly for rural community management systems. Additionally, the model identified areas with comfortable temperatures within different sections of a rural community, demonstrating its effectiveness when integrated with aerial thermography. These findings suggest the potential to estimate comfortable temperature ranges in both rural and urban dwellings, while also encouraging the development of public policies aimed at improving rural housing.Publicación Analysis of the Restoration of Distribution Substations: A Case Study of the Central–Western Division of Mexico.(MDPI, 2024-08-21) Sánchez-Ixta, Carlos; Vázquez-Abarca, Juan Rodrigo; López-Sosa, Luis Bernardo; Golpour, ImanThe studies on strategies for improving restoration times in electrical distribution systems are extensive. They have theoretically explored the application of mathematical models, the implementation of remotely controlled systems, and the use of digital simulators. This research aims to connect conceptual studies and the implementation of improvements and impact assessment in electrical distribution systems in developing countries, where distribution technologies vary widely, by employing a comprehensive methodology. The proposed research examines the restoration times for faults in substations within general distribution networks in the central–western region of Mexico. The study comprises these stages: (a) diagnosing the electrical supply, demand, and infrastructure; (b) analyzing the electrical restoration time and the restoration index of the substations; and (c) providing recommendations and implementing pilot tests for improvements in the identified critical substations. The results revealed 12 analysis zones, including 120 distribution substations, 150 power transformers, and 751 medium voltage circuits. Among the substations, 73% have ring connections, 15% have TAP connections, and 12% have radial connections. Additionally, 27% of the substations rely on only a single distribution line. The study identified areas with significant challenges in restoring electricity supply, particularly focusing on power transformers: 32 transformers with permanent power line failures requiring load transfer via medium voltage; 67 transformers requiring optimized restoration maneuvers due to specific characteristics; and 4 areas with opportunities to enhance the reliability of the power supply through remote-controlled link systems. The analysis resulted in the installation of 145 remote link systems, which improved restoration rates by over 40%. This approach is expected to be replicated throughout Mexico to identify improvements needed in the national distribution system.