Fault-tolerant control of a small reverse osmosis desalination plant with feed water bypass

Author(s):  
A Gambier ◽  
T Miksch ◽  
E Badreddin
2007 ◽  
Vol 40 (5) ◽  
pp. 161-166 ◽  
Author(s):  
Charles W. McFall ◽  
Panagiotis D. Christofides ◽  
Yoram Cohen ◽  
James F. Davis

2020 ◽  
Vol 42 (10) ◽  
pp. 1882-1894
Author(s):  
Reza Mehrad ◽  
Seyed Mohamad Kargar

Actuator faults are inevitable in small reverse osmosis desalination plants. It may cause energy losses and reduce the quality of the freshwater, which may endanger human life. This paper focuses on the integrated fault detection and fault-tolerant control approach. The primary motivation of this paper is to propose a novel integrated fault detection and fault-tolerant control approach. The actuator fault is estimated using the concept of parity space approach. Then the system model is updated in the fault-tolerant control block using the information of the estimated fault parameter. Moreover, the proposed approach uses the receding-horizon predictive control-bounded data uncertainties controller, which is the robust and stable variant of generalized predictive control. The remaining uncertainty caused by the model and observer is compensated by this controller. The structure of a small reverse osmosis desalination plant is deployed. In this plant, the permeate flow rate and conductivity are controlled by a retentate valve and a bypass valve, which add a small amount of inlet to the outlet. The performances of three predictive model controllers are evaluated, and a comparison is made between their computational costs, stability, and robustness. The plant is considered to be linear time-invariant and subject to model uncertainties, measurement noise, and actuator fault in the retentate valve as efficiency dropping. The results reveal the robustness of the proposed approach concerning noise and matched uncertainties as well as its accommodation to actuator fault up to 90%.


Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2772
Author(s):  
Vishwas Powar ◽  
Rajendra Singh

Plummeting reserves and increasing demand of freshwater resources have culminated into a global water crisis. Desalination is a potential solution to mitigate the freshwater shortage. However, the process of desalination is expensive and energy-intensive. Due to the water-energy-climate nexus, there is an urgent need to provide sustainable low-cost electrical power for desalination that has the lowest impact on climate and related ecosystem challenges. For a large-scale reverse osmosis desalination plant, we have proposed the design and analysis of a photovoltaics and battery-based stand-alone direct current power network. The design methodology focusses on appropriate sizing, optimum tilt and temperature compensation techniques based on 10 years of irradiation data for the Carlsbad Desalination Plant in California, USA. A decision-tree approach is employed for ensuring hourly load-generation balance. The power flow analysis evaluates self-sufficient generation even during cloud cover contingencies. The primary goal of the proposed system is to maximize the utilization of generated photovoltaic power and battery energy storage with minimal conversions and transmission losses. The direct current based topology includes high-voltage transmission, on-the-spot local inversion, situational awareness and cyber security features. Lastly, economic feasibility of the proposed system is carried out for a plant lifetime of 30 years. The variable effect of utility-scale battery storage costs for 16–18 h of operation is studied. Our results show that the proposed design will provide low electricity costs ranging from 3.79 to 6.43 ¢/kWh depending on the debt rate. Without employing the concept of baseload electric power, photovoltaics and battery-based direct current power networks for large-scale desalination plants can achieve tremendous energy savings and cost reduction with negligible carbon footprint, thereby providing affordable water for all.


2020 ◽  
Vol 53 (2) ◽  
pp. 16561-16568
Author(s):  
Mariam Elnour ◽  
Nader Meskin ◽  
Khlaed M. Khan ◽  
Raj Jain ◽  
Syed Zaidi ◽  
...  

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