Variable operation of a renewable energy-driven reverse osmosis system using model predictive control and variable recovery: Towards large-scale implementation

Abstract

Powering Reverse Osmosis (RO) systems with Renewable Energy (RE) is essential for decarbonising water production. Integration of RE requires large-scale RO plants to operate efficiently using variable power. Nevertheless, variable operation (involving matching the RO load to available power without battery back-up) has only been implemented for small-scale systems. This paper presents a variable-speed operation technique suitable for large-scale RO systems using an optimised operational strategy and a Model Predictive Controller (MPC). The technique was validated using a laboratory test rig having comparable performance to large-scale systems. A dynamic plant model was used to design the operational strategy and control system. Several operational strategies were explored for varying the operating parameters according to power available from a RE source. An advanced control system based on MPC was designed and compared to a conventional Proportional-Integral-Differential controller. The results showed that operation at variable recovery with constant brine flowrate delivered the lowest specific energy consumption and widest operation range for a system with an isobaric pressure exchanger. The MPC controller improved the settling time for a 10% step-change in permeate flowrate by 47%. Moreover, it improved energy utilisation, giving a 2.35% increase in hourly permeate production for a defined power input time-series.

Publication DOI: https://doi.org/10.1016/j.desal.2022.115715
Divisions: College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Engineering Systems and Supply Chain Management
College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences > School of Engineering and Technology
College of Engineering & Physical Sciences
Additional Information: © 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license 4.0
Uncontrolled Keywords: reverse osmosis,renewable energy,Variable operation,model predictive control,wind energy,Model predictive control,Wind energy,Renewable energy,Reverse osmosis,Water Science and Technology,Mechanical Engineering,Chemical Engineering(all),Chemistry(all),Materials Science(all),SDG 7 - Affordable and Clean Energy
Publication ISSN: 1873-4464
Full Text Link:
Related URLs: https://www.sci ... 1709?via%3Dihub (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2022-06-15
Published Online Date: 2022-03-30
Accepted Date: 2022-03-15
Authors: Mito, Mohamed (ORCID Profile 0000-0001-5851-8513)
Ma, Xianghong (ORCID Profile 0000-0003-4957-2942)
Albuflasa, Hanan
Davies, Philip A

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License: Creative Commons Attribution Non-commercial No Derivatives


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