Iman Janghorban Esfahani

Efficient thermal desalination technologies with renewable energy systems: A state-of-the-art review

Due to the current fossil fuel crisis and associated adverse environmental impacts, renewable energy sources (RES) have drawn interest as alternatives to fossil fuels for powering water desalination systems. Over the last few decades the utility of renewable energy sources such as solar, geothermal, and wind to run desalination processes has been explored. However, the expansion of these technologies to larger scales is hampered by techno-economic and thermo-economic challenges. This paper reviews the state-of-the-art in the field of renewable energy-powered thermal desalination systems (RE-PTD) to compare their productivity and efficiency through thermodynamic, economic, and environmental analyses. We performed a comparative study using published data to classify RE-PTD systems technologies on the basis of the energy collection systems that they use. Among RE-PTD systems, solar energy powered-thermal desalination systems demonstrate high thermo-environ-economic efficiency to produce fresh water to meet various scales of demand.

Parametric analysis and optimization of combined gas turbine and reverse osmosis system using refrigeration cycle

Abstract This study proposes a systematic approach to analyzing and optimizing combined gas turbine (GT) and reverse osmosis (RO) systems. Two systems combining RO to produce freshwater and a GT power plant to generate the required power for the RO system were modeled. In the first system, the coupling between the RO and the power plant was only mechanical; while in the second system, the coupling was both mechanical and thermal, using a refrigeration cycle. The effects of seawater temperature and intake air temperature on the freshwater production of the systems were investigated and their optimal values were calculated. Economic modeling was applied in order to calculate the unit product cost of freshwater. The second system, with two RO units under optimal operation conditions, can increase freshwater production by 26% and save 21% in the production cost of 1 m3of freshwater as compared to the first system as a base system.

Evaluation of multivariate statistical analyses for monitoring and prediction of processes in an seawater reverse osmosis desalination plant

Our aim was to analyze, monitor, and predict the outcomes of processes in a full-scale seawater reverse osmosis (SWRO) desalination plant using multivariate statistical techniques. Multivariate analysis of variance (MANOVA) was used to investigate the performance and efficiencies of two SWRO processes, namely, pore controllable fiber filter-reverse osmosis (PCF-SWRO) and sand filtration-ultra filtration-reverse osmosis (SF-UF-SWRO). Principal component analysis (PCA) was applied to monitor the two SWRO processes. PCA monitoring revealed that the SF-UF-SWRO process could be analyzed reliably with a low number of outliers and disturbances. Partial least squares (PLS) analysis was then conducted to predict which of the seven input parameters of feed flow rate, PCF/SF-UF filtrate flow rate, temperature of feed water, turbidity feed, pH, reverse osmosis (RO)flow rate, and pressure had a significant effect on the outcome variables of permeate flow rate and concentration. Root mean squared errors (RMSEs) of the PLS models for permeate flow rates were 31.5 and 28.6 for the PCF-SWRO process and SF-UF-SWRO process, respectively, while RMSEs of permeate concentrations were 350.44 and 289.4, respectively. These results indicate that the SF-UF-SWRO process can be modeled more accurately than the PCF-SWRO process, because the RMSE values of permeate flowrate and concentration obtained using a PLS regression model of the SF-UF-SWRO process were lower than those obtained for the PCF-SWRO process.

Localized indoor air quality monitoring for indoor pollutants’ healthy risk assessment using sub-principal component analysis driven model and engineering big data

Indoor air quality (IAQ) in subway systems shows periodic dynamics due to the number of passengers, train schedules, and air pollutants accumulated in the system, which are considered as an engineering big data. We developed a new IAQ monitoring model using a sub-principal component analysis (sub-PCA) method to account for the periodic dynamics of the IAQ big data. In addition, the IAQ data in subway systems are different on the weekdays and weekend due to weekly effect, since the patterns of the number of passengers and their access time on the weekdays and weekend are different. Sub-PCA-based local monitoring was developed for separating the weekday and weekend environmental IAQ big data, respectively. The monitoring results for the test data at the Y-subway station clearly showed that the proposed method could analyze an environmental IAQ big data, improve the monitoring efficiency and greatly reduce the false alarm rate of the local on-line monitoring by comparison with the multi-way PCA.

Assessment of environmental data quality and its effect on modelling error of full-scale plants with a closed-loop mass balancing

Environmental plants are notorious for poor data quality and sensor reliability due to the hostile environment in which the measurement equipment has to function, where the measurements and flow rate equipment in plants must be mutually consistent. The aim of this study is to detect any error in the measured data in an environmental plant and reconcile the data with some gross errors by using a closed data reconciliation of mass balance and the Lagrange multiplier method. A data reconciliation method based on closed-loop mass balance is suggested in order to reduce or remove error within data and obtain reliable process data. The proposed method is applied to a full-scale plant to detect the gross error in measured data, investigate the effects of erroneous data on modelling errors and compare the modelling performances of the faulty data and reconciled data. The results show that the proposed method can efficiently detect any gross error in data, estimate the error-free data by a reconciliation method and enhance the modelling accuracy by using reconciled data. This study provides a simple way to incorporate prior knowledge of plant modelling of a closed-loop mass balancing to identify any gross error and reconcile the faulty measurements.

Performance assessment and system optimization of a combined cycle power plant (CCPP) based on exergoeconomic and exergoenvironmental analyses

We propose a systematic approach for performance evaluation and improvement of a combined cycle power plant (CCPP). Exergoeconomic and exergoenvironmental analyses are used to assess CCPP performance and suggest improvement potentials in economic and environmental aspects, respectively. Economic and environmental impacts of individual system components are calculated by cost functions and life cycle assessments. Both analyses are based on a CCPP case study located in Turkey, which consists of two gas turbine cycles and a steam turbine cycle with two different pressure heat recovery units. The results of the exergoeconomic analysis indicate that the combustion chamber and condenser have a high performance improvement potential by increasing capital cost. Furthermore, the exergoenvironmental analysis shows that the exergy destruction of the steam turbine and combustion chamber and/or the capacity of heat recovery units must be reduced in order to improve environmental performance. This study demonstrates that combined exergoeconomic and exergoenvironmental analyses are useful for finding improvement potentials for system optimization by simultaneously evaluating economic and environmental impacts.

Control performance evaluation of reverse osmosis desalination system based on model predictive control and PID controllers

AbstractIn this study, to design an efficient control system for a Reverse osmosis (RO) desalination plant, a model predictive control (MPC) was established and compared to the obtained control system based on proportional–integral–derivative (PID) controllers. In order to control two controlled variables, namely permeate flow rate and conductivity, two PID controllers’ parameters were tuned based on the internal model control (IMC) rule and an MPC controller was established using the dynamic matrix control algorithm. The control performance assessment of both PID and MPC controllers were carried out using prediction error approach and their control performances were compared to that of the PID controllers which tuned by Ziegler–Nichols rule from the literature. The results showed that among the designed controllers, the PID controllers tuned by IMC method are more capable than other controllers to control the considered RO desalination plant.