Investigation on Pressure Retarded Osmosis Process Operation, Optimization, and Hybrid Systems - AlZainati, Nahawand (ProQuest Dissertations & Theses, 2024)
An enormous amount of power is stored in the earth's waters due to the salinity variations between freshwater and seawater. Globally, around 37,300 km3 per year is the amount of river discharge, which can produce around 2 TW of renewable energy when mixed with seawater. One of the technologies employed to harvest salinity gradient energy is the Pressure Retarded Osmosis (PRO) process. Pressure retarded osmosis employs membrane technology for power generation from a pair of solutions of different salinity concentrations. It is not affected by weather conditions. Recently, more research has been published on this subject. Nevertheless, no one could successfully commercialize the PRO process to our knowledge. This study aims to determine the exact appraisal of PRO for technology feasibility and determination of the main impact of the optimum parameters on the process efficiency. Some of the objectives of this study are to build a computer model to predict the specific energy generation in a single-stage PRO system and a multistage PRO system to point out the advantages of the multistage PRO systems over conventional single-stage PRO systems.The results reveal that the maximum specific energy generation (the total energy generated by the total initial feed and draw flow rates) in the dual stage pressure retarded osmosis (DSPRO) process with Dead Sea-Sea water salinity resources was 0.83 kWh/m3, showing a 71% increase in the maximum specific power generation at optimum operating conditions compared to the conventional operating conditions of equal flow rates and hydraulic pressure half of the osmotic pressure difference. Additionally, it has been suggested that utilizing the suggested hybrid system of two-pass reverse osmosis combined with DSPRO of 45000 mg/L seawater showed an 18% energy saving over the two-pass reverse osmosis model with the same conditions. Moreover, machine learning algorithms investigated and provided significant predictive power for the membrane's intrinsic parameters and power density based on the input parameters without doing any experiments. Furthermore, it has been noticed that the water flux and the power density boosted with the number of the PRO stages for all the investigated multistage PRO systems. The highest water flux and power density resulted in four PRO stages of values equal to 6 L/m2.h and 15 W/m2, respectively. The Dead Sea and wastewater salinity gradients produced the highest net specific energy generation of 0.82 kW.h/m3over the other investigated types of salinity gradients.