A comparative evaluation of alternative optimization strategies for a novel heliostat-driven hydrogen production/injection system coupled with a vanadium chlorine cycle. (1st September 2022) - Behzadi, Amirmohammad; Gholamian, Ehsan; Alirahmi, Seyed Mojtaba; Nourozi, Behrouz; Sadrizadeh, Sasan
Highlights: A hybrid system with a novel greenhouse gas emission mitigation method is introduced. Three multi-objective optimization algorithms combined with an artificial neural network are applied. The feasibility of the system is studied under the optimal operating condition for Sevilla. The multi-objective grey wolf algorithm outperforms any other competing optimization methods. A considerably lower annual carbon dioxide emission and product energy cost is achieved. Abstract: This paper introduces an innovative and cost-effective multi-generation plant, driven by the central receiver-based concentrated solar systems, to facilitate the desired global green-transition process. The vanadium chlorine thermochemical cycle, which uses hydrogen instead of natural gas in the combustion chamber, is used as an innovative approach for reducing greenhouse gas emissions. The proposed system also includes a thermoelectric generator (TEG) for excess power generation and a multi-effect desalination (MED) unit to reduce exergy loss. The suggested system's technological, economic, and environmental metrics are analyzed and compared to a similar system that stores the created hydrogen rather than burning it in the combustion chamber. Furthermore, the viability of the studied model is investigated under the optimal operating condition, using the example of Sevilla in order to make the conclusions more reliable. According to the findings, the suggested novel configuration is a better alternative in terms of cost and environmental impact owing to decreased product energy costs and CO2 emissions. The outcomes further indicate that the substitution of the condenser with TEG leads to considerably higher power production. According to the optimization findings, the multi-objective grey wolf algorithm is the best optimization strategy compared to the non-dominated genetic and particle swarm approaches. At the best optimization point, 2.5% higher exergy efficiency, 1 /GJ cheaper product energy cost, and 0.12 kg/kWh lower levelized CO2 emission are achieved compared to the operating condition. The Sankey diagram indicates that the solar heliostat system has the highest irreversibility. The exergy analysis results further reveal that the flue gas condensation process through the Rankine cycle and MED unit lead to a 53.2% reduction in exergy loss. Finally, considerable CO2 emission reductions show that the suggested new method is an effective solution for cleaner energy production in warmer climate countries.