Automation and process control of reverse osmosis plants using soft computing methodologies

Abstract

At a time of intensive demand for producing fresh water at a reasonable cost, addressing automation, process control and cost optimization of desalination plants have become increasingly evident. Large-scale direct seawater reverse osmosis (RO) plants must perform at high standards due to the increasing cost of high quality water production, high equipment utilization, and rising government regulations on labor protection and the environment. In this keynote presentation, the recent innovation and technological advances in the design and implementation of soft computing methodologies for desalination processes are addressed. Such advances are mainly due to the recent developments of intelligent control design approaches for the integration of sensory information, computation, human reasoning and decision making. The principal partners in such an intelligent system include fuzzy logic (FL), neural network (NN), generic algorithms (GA) and probabilistic reasoning (PR). Various issues which is related to the design and implementation of soft computing methodologies including the trade-off between tolerance, precision and uncertainty are also addressed. As a case study, the design and implementation of an intelligent system for a direct seawater RO system located near Atlantic Ocean at Boca Raton, Florida is presented. The operation of the prototype plant indeed demonstrated the effective and optimum performance of the design for two types of membrane modules, spiral wound (SW) and hollow fine fiber (HFF), under forced diverse operating conditions. The system has achieved a constant recovery of 30% and salt passage of 1.026% while salt concentration of six major salts were kept below their solubility limits at all time. The implementation of the proposed intelligent control methodology has achieved a 5% increase in availability and reduction in manpower requirements as well as reduction in overall chemical consumption of the plant. Therefore, it is believed that the cost of producing water can be decreased using the proposed fully automated control strategy.