Application of Multi-Objective Bayesian Optimization to Elucidate the Trade-Off Between the Solar Fraction and Cost of Parabolic Trough Solar Industrial Process Heat with Thermal Energy Storage

Level of Education of Students Involved

Undergraduate

Faculty Sponsor

Luke Venstrom

College

Engineering

Discipline(s)

Mechanical Engineering

ORCID Identifier(s)

0009-0002-1193-0206

Presentation Type

Oral Presentation

Symposium Date

Spring 4-25-2024

Abstract

Decarbonizing process heat in the industrial and building sectors is the next frontier in the energy transition. With over 50% of industrial processes requiring process heat at temperatures below 300℃, parabolic trough concentrating solar collectors and other moderately concentrating solar systems hold out promise as technology to usher in a transition away from fossil-based heat sources. Prior studies consider two plant designs, one with a fixed size of the solar field without thermal energy storage and one with a larger solar field size with thermal energy storage fixed in size. In the present work, we demonstrate how to extend prior techno-economic modeling of solar industrial process heat to incorporate optimization of the plant design for the competing objectives of minimum levelized cost of heat and the number of hours in a year the plant is able to provide the process heat demain considering several design parameters, including the size of the solar field and the size of the thermal energy storage system. A multi-objective Bayesian optimization (MOBO) algorithm is employed in conjunction with the System Advisor Model (SAM) parabolic trough collector plant model to guide the selection of design parameters towards those that lead to Pareto solutions, solutions for which the annual operational hours cannot be increase by changing the design parameters without also increasing the levelized cost of the process heat.

Biographical Information about Author(s)

Mario is studying mechanical engineering at Valparaiso University. He was born and raised in Valparaiso, IN. The research presented is a culmination of work that started in the summer of 2023 and continued through the 2023/24 academic year.

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