Coupled modeling and process optimization in a genetic-algorithm paradigm for reverse osmosis dialysate production plant

Abstract

The integrity of dialysis procedure is dependent on multivariate factors of which the production of ultrapure dialysate is considered a vital criterion in the spate of the growing rate of renal-related hospitalization. Hence the need to maintain equilibrium between process conditions and configuration of operational factors is attributed to ensuring dialysate purity under a reverse osmosis technique is an identified focus for research. This study is designed to model and optimize osmotic pressure, specific energy, and permeate water quality in a reverse osmosis (RO) plant intended to improve dialysate quality by utilizing established computational resources. The simulation of feed pressure, feed temperature, concentrate recycle and membrane type, on recovery percentage, permeate total dissolved solids concentration (TDS), cost of operation, and energy consumption, is implemented using Water Application Value Engine (WAVE) Software. The optimization efficiencies are analyzed via an artificial neural network (ANN) type generic algorithm (GA) scheme in the water purification model on the architecture of Neural Power Software. The ANN model R2 for the three outputs, permeate recovery, permeate total dissolved solids concentration (TDS) and specific energy, is 1, 0.99997 and 0.99999 respectively, while RMSE for ANN is estimated as 0.0213, 0.0248, and 0.0085 respectively. The optimum design shows a net energy saving of 2.4% can be achieved, accompanied by a 24% reduction in operating cost, a 14% reduction in permeate TDS, and a 20% increase in permeate recovery. The study achieves energy minimization, cost-effectiveness, and process integrity under a modified process design for RO-mediated ultrapure dialysate production suitable for highly sensitive applications such as clinical hemodialysis systems.