Persona:
Marcos del Cano, José Daniel

Foto de perfil
Dirección de correo electrónico
jdmarcos@ind.uned.es
ORCID
0000-0002-2703-0918
Fecha de nacimiento
Proyectos de investigación
Unidades organizativas
Puesto de trabajo
Apellidos
Marcos del Cano
Nombre de pila
José Daniel
Nombre

Filtros

2024 - 20262
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Ajustes

Autor: Barbero Fresno, Rubén ×

Resultados de la búsqueda

Mostrando 1 - 2 de 2
  • Miniatura
    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 Europea
    This 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.
  • Miniatura
    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 Europea
    This 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.