Abstract This paper proposes an economic and environmental analysis for the design of solar heat systems and their incorporation in the industrial sector. A novel computational methodology is implemented to… Click to show full abstract
Abstract This paper proposes an economic and environmental analysis for the design of solar heat systems and their incorporation in the industrial sector. A novel computational methodology is implemented to help the sustainable integration of parabolic trough collectors photothermal technology into the low and medium enthalpy industrial activities. A multivariate artificial neural network that involves environmental, operational, and economic aspects was used to transfer the phenomenon under study into a simple and fast computing multi-output mathematical model. The optimal trade-off between environmental benefits and investment viability of the hybrid solar plant’s design is obtained by a multi-objective optimization process. The objective functions consider the maximization in CO2 mitigation and net present value, and the minimization of the total life-cycle cost. The final optimal result is selected by using the TOPSIS decision-making method. Besides, a sensitivity analysis is conducted to report the system performance with respect to back-up boiler fuel type, climate region, the volume of the storage tank, and solar field area. The work contemplates the case study of a solar thermal plant based on parabolic collectors integrated into a pre-existing industrial process in Mexico. The study considered four of the most common climatic regions and the most representative heating fossil fuels in the national industrial sector. Based on the results, the best profitability and CO2 mitigation are achieved in warm climate regions. Moreover, diesel was identified as the most profitable back-up fuel scenario and natural gas as the least viable. Analysis of the energy contribution describes that implementation of parabolic trough collectors in the presented industrial process covers just over 40%–80% of the energy required, depending on the climate region. The presented methodology constitutes a rapid and low-cost computational tool which facilitates decision making for the implementation of solar heat industrial process systems. Moreover, it can be applied to other industrial processes with several photothermal technologies for clean heat generation.
               
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