ABSTRACT
The need for desalinated seawater and reclaimed wastewater is increasingly becoming critical for an ever-growing world population requiring higher demands for fresh water. This chapter discusses the principles of the non-linear programming solution methodology as applied to the superstructure optimisation problem at hand. It focuses on the genetic algorithm optimisation methodology as applied to a Reverse osmosis (RO) process in wastewater treatment. Several case studies relating to the optimisation of RO process performance can be found in the open literature. These include the investigation of the transport phenomena of water and solute through the membrane using improved mathematical models and identifying the optimum set of operating variables. The sequential quadratic programming method can be used to solve steady state optimisation problems and dynamic optimisation by implementing a first-order Taylor’s series approximation around an initial point specified in the process. The state-space optimisation framework was adopted for structural representation by considering continuous and discrete variables to minimise the total annualised cost.
