Yes / The water permeability constant, (Kw) is one of many important parameters that affect optimal design and operation of RO processes. In model based studies, e.g.within the RO process model, estimation of Kw is therefore important. There are only two available literature correlations for calculating the dynamic Kw values. However, each of them are only applicable for a given membrane type, given feed salinity over a certain operating pressure range. In this work, we develop a time dependent neural network (NN) based correlation to predict Kw in RO desalination processes under fouling conditions. It is found that the NN based correlation can predict the Kw values very closely to those obtained by the existing correlations for the same membrane type, operating pressure range and feed salinity. However, the novel feature of this correlation is that it is able to predict Kw values for any of the two membrane types and for any operating pressure and any feed salinity within a wide range. In addition, for the first time the effect of feed salinity on Kw values at low pressure operation is reported. While developing the correlation, the effect of numbers of hidden layers and neurons in each layer and the transfer functions is also investigated.

Desalination technology provides fresh water to the arid regions around the world. Multi-Stage Flash (MSF) distillation process has been used for many years and is now the largest sector in the desalination industry. Top Brine Temperature (TBT) (boiling point temperature of the feed seawater in the first stage of the process) is one of the many important parameters that affect optimal design and operation of MSF processes. For a given pressure, TBT is a function of Boiling Point Temperature (BPT) at zero salinity and Temperature Elevation (TE) due to salinity. Modelling plays an important role in simulation, optimisation and control of MSF processes and within the model, calculation of TE is therefore important for each stages (including the first stage, which determines the TBT). Firstly, in this work, several Neural Network (NN) based correlations for predicting TE are developed. It is found that the NN based correlations can predict the experimental TE very closely. Also predictions of TE by the NN based correlations were found to be good when compared to those obtained using the existing correlations from the literature. Secondly, a hybrid steady state MSF process model is developed using gPROMS modelling tool embedding the NN based correlation. gPROMS provides an easy and flexible platform to build a process flowsheet graphically. Here a Master Model connecting (automatically) the individual unit model (brine heater, stages, etc.) equations is developed which is used repeatedly during simulation and optimisation. The model is validated against published results. Seawater is the main source raw material for MSF processes and is subject to seasonal temperature variation. With fixed design the model is then used to study the effect of a number of parameters (e.g. seawater and steam temperature) on the freshwater production rate. It is observed that, the variation in the parameters affect the rate of production of fresh water. How the design and operation are to be adjusted to maintain a fixed demand of fresh water through out the year (with changing seawater temperature) is also investigated via repetitive simulation. Thirdly, with clear understanding of the interaction of design and operating parameters, simultaneous optimisation of design and operating parameters of MSF process is considered via the application MINLP technique within gPROMS. Two types of optimisation problems are considered: (a) For a fixed fresh water demand throughout the year, the external heat input (a measure of operating cost) to the process is minimised; (b) For different fresh water demand throughout the year and with seasonal variation of seawater temperature, the total annualised cost of desalination is minimised. It is found that seasonal variation in seawater temperature results in significant variation in design and some of the operating parameters but with minimum variation in process temperatures. The results also reveal the possibility of designing stand-alone flash stages which would offer flexible scheduling in terms of the connection of various units (to build up the process) and efficient maintenance of the units throughout the year as the weather condition changes. In addition, operation at low temperatures throughout the year will reduce design and operating costs in terms of low temperature materials of construction and reduced amount of anti-scaling and anti-corrosion agents. Finally, an attempt was made to develop a hybrid dynamic MSF process model incorporating NN based correlation for TE. The model was validated at steady state condition using the data from the literature. Dynamic simulation with step changes in seawater and steam temperature was carried out to match the predictions by the steady state model. Dynamic optimisation problem is then formulated for the MSF process, subjected to seawater temperature change (up and down) over a period of six hours, to maximise a performance ratio by optimising the brine heater steam temperature while maintaining a fixed water demand.

Desalination technology provides fresh water to the arid regions around the world. Multi-Stage Flash (MSF) distillation process has been used for many years and is now the largest sector in the desalination industry. Top Brine Temperature (TBT) (boiling point temperature of the feed seawater in the first stage of the process) is one of the many important parameters that affect optimal design and operation of MSF processes. For a given pressure, TBT is a function of Boiling Point Temperature (BPT) at zero salinity and Temperature Elevation (TE) due to salinity. Modelling plays an important role in simulation, optimisation and control of MSF processes and within the model, calculation of TE is therefore important for each stages (including the first stage, which determines the TBT). Firstly, in this work, several Neural Network (NN) based correlations for predicting TE are developed. It is found that the NN based correlations can predict the experimental TE very closely. Also predictions of TE by the NN based correlations were found to be good when compared to those obtained using the existing correlations from the literature. Secondly, a hybrid steady state MSF process model is developed using gPROMS modelling tool embedding the NN based correlation. gPROMS provides an easy and flexible platform to build a process flowsheet graphically. Here a Master Model connecting (automatically) the individual unit model (brine heater, stages, etc.) equations is developed which is used repeatedly during simulation and optimisation. The model is validated against published results. Seawater is the main source raw material for MSF processes and is subject to seasonal temperature variation. With fixed design the model is then used to study the effect of a number of parameters (e.g. seawater and steam temperature) on the freshwater production rate. It is observed that, the variation in the parameters affect the rate of production of fresh water. How the design and operation are to be adjusted to maintain a fixed demand of fresh water through out the year (with changing seawater temperature) is also investigated via repetitive simulation. Thirdly, with clear understanding of the interaction of design and operating parameters, simultaneous optimisation of design and operating parameters of MSF process is considered via the application MINLP technique within gPROMS. Two types of optimisation problems are considered: (a) For a fixed fresh water demand throughout the year, the external heat input (a measure of operating cost) to the process is minimised; (b) For different fresh water demand throughout the year and with seasonal variation of seawater temperature, the total annualised cost of desalination is minimised. It is found that seasonal variation in seawater temperature results in significant variation in design and some of the operating parameters but with minimum variation in process temperatures. The results also reveal the possibility of designing stand-alone flash stages which would offer flexible scheduling in terms of the connection of various units (to build up the process) and efficient maintenance of the units throughout the year as the weather condition changes. In addition, operation at low temperatures throughout the year will reduce design and operating costs in terms of low temperature materials of construction and reduced amount of anti-scaling and anti-corrosion agents. Finally, an attempt was made to develop a hybrid dynamic MSF process model incorporating NN based correlation for TE. The model was validated at steady state condition using the data from the literature. Dynamic simulation with step changes in seawater and steam temperature was carried out to match the predictions by the steady state model. Dynamic optimisation problem is then formulated for the MSF process, subjected to seawater temperature change (up and down) over a period of six hours, to maximise a performance ratio by optimising the brine heater steam temperature while maintaining a fixed water demand.

This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions.