Machine learning interfaces for modular modelling and operation-based design optimization of solar thermal systems in process industry

Abstract

The trend of introducing solar thermal systems (STSs) in process industries has resulted in a new energy paradigm– an interactive platform where there are economic benefits and motivations to address sustainable development. On the other hand, this paradigm has also introduced fluctuations and uncertainties not previously seen on the energy system, and is challenging the industry. Industrial STSs are composed of large number of interacting, nonlinear and uncertain subsystems and, therefore are complex systems. This complexity of heterogenous, stochastic, and dynamic behavior make designing of industrial STSs as well as evaluation of their management strategies challenging tasks for the existing tools. In an attempt to support integration decisions, always inefficient and insufficient, machine learning (ML) is leveraged in this research. Following the ML approach, some important findings were made. Firstly, it permits for different releases of the same/different module, which can be easily embedded, coupled exogenously or run in parallel. Thus, the operation-based design approach, both for process and supply level integration of STSs, were scalable and tractable. Distinguished from past work, the approach also allowed testing of an improved STS sizing scheme, while at the same time, enabling optimization of its control concept for adaptive and flexible operation. In the considered case study, inclusion of time-varying heat demand, process temperature and solar irradiation in STS design reduces over-dimensioning by 10–30%. Furthermore, optimizing the control concept of the overall system led to improved energy efficiency of solar field and storage respectively by 23.7% and 8.3% over the state-of-the-art methods.