Abstract
Integrating solar thermal collectors into industrial processes could be a viable way to replace the use of conventional fuels and achieve economic and environmental goals. However, there is a need to consider the detailed dynamic operation of a system with storage on a systematic control scale to fully optimize realistic system performance under variable conditions by minimizing excess energy production and maximizing annual life-cycle cost savings. In this study, we developed a TRNSYS-based dynamic statistical optimization model and evaluated FPC-based solar-assisted heating systems to develop a cost-effective system design for two industries: MOHA soft drinks and Sheba leather factories in the Tigray region, Ethiopia. Three operating loads were compared: process heat, utility heat, and utility heat and chilled water. The optimized designs resulted in significant annual life-cycle cost savings, high solar fractions, and a good margin on temperature trends where solar collector size has a greater impact. Annual cost savings per unit area of solar collector for process and utility heat were in the range of $51–90/m2 for a collector mass flow rate and storage volume of 0.01–0.02 m3/h-m2 and 0.04–0.08 m3/m2, respectively. For the utility heat and chilled water loads, the values were $49/m2 for a mass flow rate of 0.04 m3/h-m2 and a storage volume of 0.07 m3/m2. Thus, the study supports the transient analysis of solar-assisted industrial heat. The case studies have shown that the method provides optimal solutions for the use of solar thermal energy. As investment and financial sourcing remain a priority challenge, the model and case study results could help in decision-making for similar and other production capacities, regions, industries, and solar technologies.
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