Performance assessment of a solar powered hydrogen production system and its ANFIS model

Modeling, simulation and performance evaluation of a PVT system for the Kenyan manufacturing sector

Manufacturing is an end-use sector that uses the most delivered energy, accounting for around 50% of all transported fuel globally and 40% of carbon dioxide emissions worldwide. Solar photovoltaic-thermal (PVT) energy can substitute the transported energy to meet thermal and electrical energy requirements, mitigating high energy costs and climatic problems. This research aimed to develop, simulate, and evaluate the capabilities of a solar photovoltaic-thermal system for potential use in Kenya's manufacturing sector. A multistage cluster sampling technique was used in the study to characterize the manufacturing industry. Additionally, a PVT system was simulated using MATLAB Simulink to ascertain the relationship of temperature and the PV electrical efficiency. The impact of incorporating a thermal collector into the PV system on electrical, thermal, and overall system efficiency, and also the system's potential for use in thermal processes in manufacturing, were assessed. From the characterization results, the agro-processing sector dominates with 35% representation, and the small-scale thermal energy category dominates at 80%. The simulation findings show that a small temperature increase leads to a small increment in short circuit current but a significant decline in open circuit voltage. As a consequence, the maximum power (Pmax) of the PV decreases, lowering its electrical efficiency. However, the integration of PV with thermal collector improved the electrical, thermal, and the entire system efficiencies by, 16.01%, 20%, and 36.13%, respectively. More than 75% of the electrical and thermal energy processes fall in the small energy category. Hence, the PVT system is suitable for small-scale low-to-medium heat thermal energy categories or as a substitute system for higher temperature processes to raise feed water temperatures and reduction of thermal energy cost. This study gives a new approach of the application of PVT system for thermal industrial applications.

ANFIS and ANN models to predict heliostat tracking errors

The efficiency and performance of solar tower power are greatly influenced by the heliostats field. To ensure accurate tracking of reflectors often requires an evaluation of the beam reflected positions. This operation is costly time-consuming due to the number of heliostats. It is also necessary to set up a fast and less expensive method able to evaluate tracking heliostat. In this paper, prediction models based on the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) were applied to estimate rapidly and accurately heliostat error tracking. The modeling is based on the experimental data of seven different days. The input parameters are time and day number and the output is the beam reflected position following the altitude and azimuth axes. Both techniques have been able to predict the beam reflected position. A comparison of results showed that intelligent methods recorded better performance than conventional model based on geometric errors. For ANFIS model, coefficients of correlation (R²) of 0.97 is obtained compared to that of the ANN, 0.96 and 0.92 for altitude and azimuth axes respectively. The intelligent methods may be a promising alternative for predicting heliostat beam reflected the position.