Douglas T. Mugweni

Work place: Engineering Institute of Technology, Perth, WA 6005, Australia



Research Interests: Computational Engineering, Engineering, Electronic Engineering


Douglas T. Mugweni. Douglas Mugweni was born in Harare, Zimbabwe on the 4th of September 1974. Douglas obtained his first B.Eng(Honours) degree in electronic engineering from the National University of Science and Technology (NUST), in the city of Bulawayo, Zimbabwe in 1999. Twenty years later in 2020, he obtained his M.Eng in industrial automation from the Engineering Institute of Technology in Perth, Australia. He joined the Zimbabwe Power Company as a Graduate Trainee Engineer in control and instrumentation and rose to become Senior Engineer. In 2007, he moved to South Africa and joined the power utility Eskom in the Generation Division as a Control and Instrumentation Engineer based at its Camden Power Station plant in Ermelo in the province of Mpumalanga.Engineer Mugweni is registered as a professional engineer with the Engineering Council of South Africa (ECSA), a senior member of the South African Institute of Electrical Engineers (SAIEE), and a corporate member of the Institute of Engineering and Technology (IET) in UK.

Author Articles
Neural Networks-based Process Model and its Integration with Conventional Drum Level PID Control in a Steam Boiler Plant

By Douglas T. Mugweni Hadi Harb

DOI:, Pub. Date: 8 Oct. 2021

Controlling drum level is a major and crucial control objective in thermal power plant steam boilers. The drum level as a controlled variable is highly characterized by complex non-linear process dynamics as well as measurement noise and long-time delays. Developing a data-driven process model is particularly advantageous as it could be built from ongoing operational data. Such a model could be used to assist existing controllers by providing predictions regarding the drum level. The aim of this paper is to develop such a model and to propose a control architecture that can be easily integrated into existing control hardware. For that purpose, different neural networks are used, Multilayer Perceptron (MLP), Nonlinear Autoregressive Exogenous (NARX), and Long Short Term (LSTM) neural networks. LSTM and MLP were able to capture the dynamics of the process, but LSTM showed superior performance. The results demonstrate that the use of traditional machine learning criteria to evaluate a process model is not necessarily adequate. Using the model in an open-loop and a closed-loop simulation is more suitable to test its ability to capture the dynamics of the process. A novel architecture that integrates the process model within an existing closed-loop controller is proposed. The architecture uses adaptive weights to ensure that a good model is given more influence than a bad model on the controller’s output.

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