Artificial Intelligence–Based Optimization of Reverse Osmosis Systems Operation Performance

Nazif, Sara; Mirashrafi, Emad; Roghani, Bardia; Bidhendi, Gholamreza Nabi

doi:10.1061/(asce)ee.1943-7870.0001613

Cited by 9 publications

(1 citation statement)

References 45 publications

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“…Farsi and Rosen performed a multi-objective optimization case study based on an ANN for a geothermal desalination system to evaluate the trade-off between exergy efficiency and process cost [46]. Nazif et al optimized the operation of a RO system to reduce fouling, increase membrane life span, and minimize system cost using an ANN [47]. Soleimani et al employed an ANN to derive Pareto-optimal solutions for maximizing the permeate output and minimizing the fouling resistance for membrane separation of wastewater [48].…”

Section: Introductionmentioning

confidence: 99%

A Neural Network Based Superstructure Optimization Approach to Reverse Osmosis Desalination Plants

2022

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An ever-growing population together with globally depleting water resources pose immense stresses for water supply systems. Desalination technologies can reduce these stresses by generating fresh water from saline water sources. Reverse osmosis (RO), as the industry leading desalination technology, typically involves a complex network of membrane modules that separate unwanted particles from water. The optimal design and operation of these complex RO systems can be computationally expensive. In this work, we present a modeling and optimization strategy for addressing the optimal operation of an industrial-scale RO plant. We employ a feed-forward artificial neural network (ANN) surrogate modeling representation with rectified linear units as activation functions to capture the membrane behavior accurately. Several ANN set-ups and surrogate models are presented and evaluated, based on collected data from the H2Oaks RO desalination plant in South-Central Texas. The developed ANN is then transformed into a mixed-integer linear programming formulation for the purpose of minimizing energy consumption while maximizing water utilization. Trade-offs between the two competing objectives are visualized in a Pareto front, where indirect savings can be uncovered by comparing energy consumption for an array of water recoveries and feed flows.

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Section: Introductionmentioning

confidence: 99%