Shahzad, Muhammad Wakil;
Nguyen, Viet Hung;
Xu, Ben Bin;
Tariq, Rasikh;
Imran, Muhammad;
Ashraf, Waqar Muhammad;
Ng, Kim Choon;
... Sheikh, Nadeem Ahmed; + view all
(2024)
Machine Learning Assisted Prediction of Solar to Liquid Fuel Production A Case Study.
Process Safety and Environmental Protection
, 184
pp. 1119-1130.
10.1016/j.psep.2024.02.060.
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Abstract
In this era of heightened environmental awareness, the global community faces the critical challenge of climate change. Renewable energy (RE) emerges as a vital contender to mitigate global warming and meet increasing energy needs. Nonetheless, the fluctuating nature of renewable energy sources underscores the necessity for efficient conversion and storage strategies. This pioneering research focuses on the transformation of solar energy (SE) into liquid fuels, with a specific emphasis on formic acid (FA) as a case study, done in Binh Thuan, Vietnam. The paper unveils a technology designed to convert solar energy into formic acid, ensuring its stability and storage at ambient conditions. It involves detailed simulations to quantify the daily and monthly electricity output from photovoltaic (PV) systems and the corresponding mass of formic acid producible through solar energy. The simulation of a dual-axis solar tracking system for the PV panels, intended to maximize solar energy capture, is one of the project's illustrations. The elevation and azimuth angles, which are two essential tracking system parameters, are extensively studied in the present research. The project makes use of machine learning algorithms in the field of predictive modeling, specifically Artificial Neural Networks (ANN) and Support Vector Machines (SVM). These tools play a crucial role in modeling PV power output and formic acid production while accounting for a variety of influencing factors. A comparative study shows that SVM outperforms ANN in accurately predicting the production of FA and PV power generation, both of which are the major goals. This model is a predictive tool that can be used to forecast these goals based on certain causal variables. Overall, it is observed that the maximum power produced with 2-axis solar tracker was achieved in February as 2355 kW resulting in the highest formic acid production of 2.25 ×106 grams. The study's broad ramifications demonstrate solar liquid fuel technology's potential as a long-term fix in the field of renewable energy. In addition to advancing the field of renewable energy storage, the study represents a major step toward tackling the global challenge of climate change.
| Type: | Article |
|---|---|
| Title: | Machine Learning Assisted Prediction of Solar to Liquid Fuel Production A Case Study |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.psep.2024.02.060 |
| Publisher version: | https://doi.org/10.1016/j.psep.2024.02.060 |
| Language: | English |
| Additional information: | © 2024 The Author(s). Published by Elsevier Ltd on behalf of Institution of Chemical Engineers. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Solar energy, Liquid fuel, Formic acid, Two-axis solar tracking system |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10188192 |
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