Machine Learning Interfaces for Optimal Design and Control of Solar Thermal Systems in Process Industry
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Date
2022-06
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Addis Ababa University
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. Accordingly, we are observing increasing need for robust design and control solutions
that will facilitate the smooth operation and cost competitiveness of the industrial solar thermal
system (ISTS). The possibility for developing such a solution exists, but only if the necessity to
explicitly model, which might not work well, and also increase computational complexity in the
ISTS, is removed. In this dissertation work, a machine learning (ML) approach is followed for
design and control optimization of ISTSs, and is leveraged for two goals. First it is used as a
multi-modelling tool for developing heterogeneous optimization interfaces, using stochastic and
generic models. These interfaces are intended to be simple but are not simpler in order to
simultaneously address both scalability-tractability tradeoffs and model inefficiencies of
conventional methods. Afterwards, ML enabled linking up of these interfaces as building blocks
for realizing a modular optimization framework, and of integrating different layers of
functionalities. As a result, the ML approach allowed disaggregated modelling of several similar
technologies (and processes) as well as parameterizing of their inputs and local condition
differently. Using this method, it was also possible to represent distributed energy resources
(DERs) and their additional capabilities of interactions. Furthermore, it allowed replication of
simulation experiments with the same model and at varying scale levels. These are essential
features that cannot be offered by conventional methods, and can be used to improve synergy and
unlock the potentials of DERs in ISTSs.
Following the ML approach, some important findings were made. Firstly, the solutions of the
optimal design problems were scalable and tractable. This feature facilitates operation-based
designs of STSs according to the specific requirements of process(s), heat distribution networks
or existing thermal plant in industry. The approach also allowed the testing of an improved
optimal control strategy, while at the same time, enabling controller tuning or model calibration.
This capability is used to adapt an empirical solar radiation model, to serve as an efficient and
low-cost sensor that can be integrated to ISTSs in real-time. However, due to the scope and
limitation of the dissertation, these relevant findings provide mainly key design and control
strategies and points of discussion instead of benchmarked results. Therefore, it is particularly
desirable if further research could confirm these findings.
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Keywords
Design optimization., optimal control, model calibration, machine learning, solar thermal system, process industry