Modelling and optimal control of the market of a telecommunications operator

A South African GSM telecommunications market consisting of two incumbents and an entering third player, is modelled utilising a non-linear, system dynamics approach. The model calculates subscriber choice based on a calculated utility. The utility is used to obtain a probability which is fed into a Bass type differential equation relating the different states in the model to their time derivatives. The model encapsulates all the prominent postpaid price plans in the market, as well as five different demographic market segments. Model Predictive Control is used to synthesise a linear feedback controller which uses the observed market state to optimally determine a price time series for one of the operators’ products. The series will maximise Average Revenue Per User (ARPU) for the operator over the simulation time interval. Besides ARPU, the controller is also able to increase total revenue and minimise churn over the simulated interval for the optimising operator, and thus provides valuable decision support to the marketing management of such an operator.Dissertation (MEng (Electronic))--University of Pretoria, 2004.Electrical, Electronic and Computer Engineeringunrestricte

Dynamic modelling of induced draft cooling towers with parallel heat exchangers, pumps and cooling water network

In the process industries, cooling capacity is an important enabler for the facility to manufacture on specification product. The cooling water network is an important part of the over-all cooling system of the facility. In this paper a cooling water circuit consisting of 3 cooling towers in parallel, 2 cooling water pumps in parallel, and 11 heat exchangers in parallel, is modelled. The model developed is based on first principles and captures the dynamic, non-linear nature of the plant. The modelled plant is further complicated by continuous, as well as Boolean process variables, giving the model a hybrid nature. Energy consumption is included in the model as it is a very important parameter for plant operation. The model is fitted to real industry data by using a particle swarm optimisation approach. The model is suitable to be used for optimisation and control purposes.The National Research Foundation of South Africa (Grant Number 90533).http://www.elsevier.com/locate/jprocont2019-08-01hj2018Electrical, Electronic and Computer Engineerin

Hybrid nonlinear model predictive control of a cooling water network

A Hybrid Nonlinear Model Predictive Control (HNMPC) strategy is developed for temperature control and power consumption minimisation of a cooling water network. The HNMPC uses a gradient descent optimisation algorithm for the continuous manipulated variables, and an enumerated tree traversal algorithm to control and optimise the Boolean manipulated variables. The HNMPC is subjected to disturbances similar to those experienced on a real plant, and its performance compared to a continuous Nonlinear Model Predictive Control (NMPC) and two base case scenarios. Power consumption is minimised, and process temperature disturbances are successfully rejected. Monetary benefits of the HNMPC control strategy are estimated.The National Research Foundation of South Africahttp://www.elsevier.com/locate/conengprac2021-04-01hj2020Electrical, Electronic and Computer Engineerin

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Dynamic Modelling and Hybrid Non-Linear Model Predictive Control of Induced Draft Cooling Towers With Parallel Heat Exchangers, Pumps and Cooling Water Network

In the process industries, cooling capacity is an important enabler for the facility to manufacture on specification product. The cooling water network is an important part of the over-all cooling system of the facility. In this research a cooling water circuit consisting of 3 cooling towers in parallel, 2 cooling water pumps in parallel, and 11 heat exchangers in parallel, is modelled. The model developed is based on first principles and captures the dynamic, non-linear, interactive nature of the plant. The modelled plant is further complicated by continuous, as well as discrete process variables, giving the model a hybrid nature. Energy consumption is included in the model as it is a very important parameter for plant operation. The model is fitted to real industry data by using a particle swarm optimisation approach. The model is suitable to be used for optimisation and control purposes. Cooling water networks are often not instrumented and actuated, nor controlled or optimised. Significant process benefits can be achieved by better process end-user temperature control, and direct monetary benefits can be obtained from electric power minimisation. A Hybrid Non-Linear Model Predictive Control strategy is developed for these control objectives, and simulated on the developed first principles dynamic model. Continuous and hybrid control cases are developed, and tested on process scenarios that reflect conditions seen in a real plant. Various alternative techniques are evaluated in order to solve the Hybrid Non-Linear Control problem. Gradient descent with momentum is chosen and configured to be used to solve the continuous control problem. For the discrete control problem a graph traversal algorithm is developed and joined to the continuous control algorithm to form a Hybrid Non-Linear Model Predictive controller. The potential monetary benefits that can be obtained by the plant owner through implementing the designed control strategy, are estimated. A powerful computation platform is designed for the plant model and controller simulations.Thesis (PhD)--University of Pretoria, 2019.Electrical, Electronic and Computer EngineeringPhDUnrestricte