IDE recommissions desalination plant in California, USA

Exergy analysis of a reverse osmosis desalination plant in California

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.

Exergetic efficiency of NF, RO and EDR desalination plants

Exergy analysis of a combined RO, NF, andEDR desalination plant

Ionics to build/run desalination plant in California

Developing and financing new water facilities: Alternatives for desalination and reclamation plants

Planning the restoration of membranes in RO desalination using a digital twin

Providing forecasts of pressure fluctuations and changes will aid in selecting appropriate maintenance strategies to optimize efficiency and costs. This paper presents a deep-learning-based model to forecast the degradation evolution of membrane biological fouling in RO (Reverse Osmosis) systems. Although applying deep learning in forecasting still faces many challenges, applying convolutional operations in convolution 1D has yielded promising results for sequential data, particularly time series data. Thus, in this paper we study and develop the 1D convolution operation-based Temporal Convolutional Network (TCN) model to predict pressure dynamics at both ends of the RO vessel. In addition, since the deep learning technique has yet to be widely explored in this field, thus we also need to pre-process the data collected from the Carlsbad Desalination Plant in California, such as the proposed model can identify complex relationships between timestamps and pressure features. The experiment results were evaluated and compared with other existing models, such as LSTM, CNN & LSTM, and GRU. The obtain results show that the TCN-based prediction model had the slightest error in the test dataset.