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 Engineering / PhD / Unrestricted