Prediction models to analyse the performance of a commercial-scale membrane distillation unit for desalting brines from RO plants

Desalting brines from Reverse Osmosis (RO) plants is one of the most promising applications of Membrane Distillation (MD) systems. The development of accurate models to predict MD system performances plays a significant role in the design of this kind of industrial applications. In this paper, a commercial-scale Permeate-Gap Membrane Distillation (PGMD) module was modelled by means of two different approaches: Response Surface Methodology (RSM) and Artificial Neural Networks (ANN). Condenser inlet temperature, evaporator inlet temperature, feed flow rate and feed water salt concentration were selected as inputs of the model, while permeate flux and Specific Thermal Energy Consumption (STEC) were chosen as responses. The prediction abilities of both RSM and ANN models were compared with further experimental data by using the Analysis of Variance (ANOVA) and the Root Mean Squared Error (RMSE). The results show that the ANN model is able to predict in a more precise way the behaviour of the module for the whole range of input variables. Thus, ANN model was used to find the optimal operating conditions, for the module operating at feed water salinity of 70 and 105 g/L, concentrations that can be reached when desalting RO brines

Harnessing the power of neural networks for the investigation of solar-driven membrane distillation systems under the dynamic operation mode

Accurate modeling of solar-driven direct contact membrane distillation systems (DCMD) can enhance the commercialization of these promising systems. However, the existing dynamic mathematical models for predicting the performance of these systems are complex and computationally expensive. This is due to the intermittent nature of solar energy and complex heat/mass transfer of different components of solar-driven DCMD systems (solar collectors, MD modules and storage tanks). This study applies a machine learning-based approach to model the dynamic nature of a solar-driven DCMD system for the first time. A small-scale rig was designed and fabricated to experimentally assess the performance of the system over 20 days. The predictive capabilities of two neural network models: multilayer perceptron (MLP) and long short-term memory (LSTM) were then comprehensively examined to predict the permeate flux, efficiency and gain-output-ratio (GOR). The results showed that both models can efficiently predict the dynamic performance of solar-driven DCMD systems, where MLP outperformed the LSTM model overall, especially in the prediction of efficiency. Additionally, it was indicated that the accuracy of the models for the prediction of GOR can be significantly improved by increasing the size of the dataset

Sustainability ranking of desalination plants using Mamdani Fuzzy Logic Inference Systems

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability

A feedback control system with reference governor for a solar membrane distillation pilot facility

This work presents the development of a feedback control system for a pilot membrane distillation facility powered with solar energy located at Plataforma Solar de Almería (PSA), Spain. The control system allows to fix a suitable operating temperature at the inlet of the distillation system, improving the operation quality. Four direct control schemes based on Proportional Integral (PI) controllers and Feedforward (FF) are designed as well as a reference governor which generates temperature references for the heat generation circuit direct control layer. Simulations and experimental tests are shown to demonstrate the effectiveness of the proposed scheme

Mathematical and optimization modelling in desalination: State-of-the-art and future direction

The growing water demand across the world necessitates the need for new and improved processes as well as for a better understanding of existing processes. This level of understanding includes predicting system performance in scenarios that cannot always be evaluated experimentally. Mathematical modelling is a crucial component of designing new and improved engineering processes. Through mathematically modelling real life systems, we gain a deeper understanding of processes while being able to predict performance more effectively. Advances in computational capacity and the ease of assessing systems allow researchers to study the feasibility of various systems. Mathematical modelling studies enable optimization performance parameters while minimizing energy requirements and, as such, have been an active area of research in desalination. In this review, the most recent developments in mathematical and optimization modelling in desalination are discussed with respect to transport phenomena, energy consumption, fouling predictions, and the integration of multiple scaling evolution on heat transfer surfaces has been reviewed. Similarly, developments in optimization of novel reverse osmosis (RO) configurations have been analyzed from an energy consumption perspective. Transport models for membrane-based desalination processes, including relatively less understood processes such as nanofiltration and forward osmosis are presented, with recent modifications to allow for different solutes and solutions. Mathematical modelling of hybrid systems integrated with RO has also been reviewed. A survey of the literature shows that mathematical and optimization modelling of desalination processes is an exciting area for researchers in which future scholarship includes coupling of renewable energy systems with desalination technologies, as well as more advanced descriptions of fouling evolution other than that of cake filtration in membrane-based processes

Membrane distillation: Solar and waste heat driven demonstration plants for desalination

The development of small to medium size, autonomous and robust desalination units is needed to establish an independent water supply in remote areas. This is the motivation for research on alternative desalination processes. Membrane distillation (MD) seems to meet the specific requirements very well. This work is focused on experimental studies on full scale demonstration systems, utilizing a parallel multi MD-module setup. Three different plant concepts are introduced, one of them is waste heat driven and two of them are powered by solar thermal collectors. Design parameters and system design are presented. After the analysis of plant operation a comparison among the plants as well as a comparison with laboratory experiments is carried out and discussed. Impact of different feed flow rates, salinities, operating hours and process temperatures are taken into consideration and put into relation. GOR values and specific thermal heat demand are derived and compared. Energy balances of all three plants are given, uncovering heat losses and identifying room for improvemen

Generic Model Control (GMC) in Multistage Flash (MSF) Desalination

YesMultistage Flash Desalination (MSF) is currently facing an enormous challenge in cutting of the cost: within the last few years, the MSF experienced a gradual decline in investment compared to other techniques of desalting water and thus, a significant improvement is required to remain attractive for capital investors. Improved process control is a cost effective approach to energy conservation and increased process profitability. In this work, a dynamic model is presented using gPROMS model builder to optimize and control MSF process. The Proportional Integral Derivative Controller (PID) and Generic Model Control (GMC) are used successfully to control the Top Brine Temperature (TBT) and the Brine Level (BL) in the last stage at different times of the year. The objectives of this study are: firstly, to obtain optimum TBT and BL profiles for four different seasons throughout the year by minimizing the Total Seasonal Operating Cost (TSOC); secondly, to track the optimum TBT and BL profiles using PID and GMC controllers with and without the presence of constraints; thirdly, to examine how both types of controllers handle the disturbances which occur in the plant. The results are promising and show that GMC controller provides better performance over conventional PID controller to handle a nonlinear system