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

Fouling of dairy components on hydrophobic polytetrafluoroethylene (PTFE) membranes for membrane distillation

This study investigates fouling of membranes during membrane distillation (MD) of two model dairy feeds — skim milk and whey, as well as their major single components. Every MD experiment was conducted for 20 hat 54 C feed inlet temperature and 5 C permeate inlet temperature using PTFE membranes. Performance was assessed in terms of throughput (flux) and retention efficiency.Skim milk flux was found to be lower but stable overtime compared to whey.The study using single components as well as combinations the reofrevealed that fouling was primarily driven by proteins and calcium, but only in combination.Lactose also played a role to a lesser extent in the protein/membrane interactions, possibly due to preferential hydration,but did not interact with the membrane polymer directly. However lactose was found to deposit once an anchorpoint to the membrane was established by other components. Skim milk showed strong adhesion from its principle proteins, caseins;however salts were needed to form a thick and dense cake layer.Caseins seem to form a layer on the membrane surface that prevents other components from interacting with the membrane polymer.Wheyproteins, on the other hand, deposited to alesse rextent. In general membrane distillation was found to be a process that generates high quality water with retention of all tested components >99% while simultaneously concentrating whey or skim milk

Enhanced performance of direct contact membrane distillation via selected electrothermal heating of membrane surface

Membrane distillation (MD) is a thermally driven separation process with great potential, but is currently limited by low energy efficiency. Heating of the entire circulating feed represents a major source of energy consumption in MD. Here, we present electrically conductive carbon nanostructure (CNS-) coated polypropylene (PP) membranes as a possible candidate to mitigate energy consumption through selected electrothermal heating of the membrane surface. A membrane for MD was coated with CNS using a tape casting technique. The resulting CNS-PP membrane is hydrophobic, and its smaller pore size and narrow pore size distribution resulted in a higher liquid entry pressure compared to the uncoated PP membrane. An increase in surface temperature was observed when a current was passed through the conductive CNS layer. The CNS layer on the PP membrane acts as an electrothermal heater when an AC potential is applied, and the rate of heating is proportional to the amplitude of applied AC potential. We applied electrothermal heating of these membranes to desalination by direct contact membrane distillation, in conjunction with heating of the circulating feed, and compared the performance with and without application of AC bias at three feed temperatures viz. 40, 50 and 60 °C. Applying a potential across the CNS layer increased permeate flux by 75, 76 and 61% at feed temperatures of 40, 50 and 60 °C respectively, while maintaining a salt rejection of >99%. This increase in flux is accompanied by a reduction in specific energy consumption of greater than 50% for all three feed temperatures. By combining electrothermal surface heating with MD, this study paves the way for smart, low-energy MD systems

Small scale desalination technologies: A comprehensive review

In recent decades, problems related to fresh water has become a very important issue for humans. Small-scale desalination (SSD) systems, besides large-scale desalination (LSD) systems, fulfil an important role in meeting freshwater demand by eliminating the cost of transmission and have the advantage of treating water on-site. In this study, for the first time, a comprehensive review of previous studies has been carried out on SSD systems (less than 25 m3/d water production). These systems are powered using renewable, non-renewable or hybrid sources of energy, incorporating different treatment technologies such as: reverse osmosis (RO); electro dialysis (ED); capacitive deionization (CDI); membrane desalination (MD); humidification–dehumidification processes (HDH); multi-effect desalination (MED); and hybrid technologies, including a combination of RO-UF, RO-ED and RO-MED. The advantages and drawbacks of the systems that operate using fossil fuels and renewable energy (RE) systems have been studied, considering membrane, evaporation and salinity features. Among these, solar-based desalination systems are the most popular. Accordingly, numerous studies on RO, ED, MD, HDH and MED technologies for solar-SSD systems have been compared in terms of their freshwater productivity, energy consumption and cost of produced water. Attention has also been paid to SSD systems powered via wind, geothermal, tidal and hybrid energies. It has been determined that the RO system holds the largest market share in both non-renewable (25 %) and renewable energy (40 %) systems. In addition, a comparison of low-cost SSD and LSD systems shows that SSD systems are economically competitive with LSD systems. The outlook for the future shows that the use of SSD systems powered using non-renewable energy is likely to decrease, except in areas where energy costs are very low. In addition, the use of solar-SSD systems is likely to increase, where systems that operate solely on wind or geothermal energy will be replaced by hybrid renewable energy systems

Artificial Intelligence-Based Optimization of Industrial Membrane Processes

AbstractArtificial intelligence (AI) is gaining acceptance for modern control systems in various applications in daily life including the Chemical process industry. Above all, application of AI is increasing in the field of membrane-based treatment where it shows great potential until now. Membrane separations are generally recognized as energy-efficient processes. In particular, membrane desalination, forward osmosis, energy generation, and biomass treatment have shown substantial potential in modern industries, such as wastewater treatment, pharmaceuticals, petrochemicals, and natural products. All these industries consume more than 20% of total energy consumption in the world. Moreover, the laboratory research outcomes illuminate the way to better membrane design and development, including advanced process control and optimization. The membrane processes with existing technologies for a sustainable environment could be integrated with the AI model. This review summarizes several membrane-based water treatment designs and plant performances where artificial intelligence is being used to minimize waste generation and lead to cleaner production

Direct Contact Membrane Distillation for Desalination of High Salinity Brines: Fundamentals and Application

Membrane distillation is a cost effective solution for the treatment of high salinity wastewater where reverse osmosis is not feasible, especially if waste heat is utilized for its operation. One such example of high salinity wastewater is produced water generated as a consequence of hydraulic fracturing used for natural gas extraction from unconventional onshore resources. The objective of this study was to evaluate the feasibility of treating such high salinity wastewaters by employing membrane distillation while using waste heat as a source of energy to drive the process and by using produced water as an example wastewater. When commercially available membranes were tested in a direct contact membrane distillation (DCMD) system, the membranes exhibited excellent rejections of ions with no flux degradation due to fouling. However, it was found that concentration polarization (CP) was significantly higher when treating high salinity feed water and the CP effect could not be accurately estimated using current methods of calculation. Based on lab scale studies, an ASPEN Plus simulation was developed to simulate the operation of large scale systems and estimate energy requirements of the DCMD process to treat produced water in the state of Pennsylvania by using exhaust stream of Natural Gas Compressor Station (NG CS) as the waste heat source. The results from this study suggested that the waste heat available from NG CS is sufficient to treat all the produced water generated in Pennsylvania regardless of its initial salinity. In an attempt to study the effect of concentration polarization that was found to be significant during DCMD tests with produced water, and has been neglected in most membrane distillation studies, a novel spatially resolved non-intrusive spectrophotometric method was developed to measure the concentration profile of solute near the membrane surface in a direct contact membrane distillation system. The objective was to probe the concentration profile of solute and analyze the impact of operating parameters, such as feed concentration, hydrodynamic conditions and feed temperature, on the solute concentration profile in the boundary layer. A key finding of this study is that the conventional approach of estimating the effect of concentration polarization severely under predicts the boundary layer thickness (BLT) and concentration polarization coefficient (CPC). The results of this study highlight the need to develop new methods to estimate the BLT and CPC as the conventional approach of mass transfer analogy of heat transfer does not agree with experimental observations obtained for a membrane distillation system

Redesign of the milk powder production chain: assessment of innovative technologies

The dairy industry is energy intensive, mainly due to the use of thermal processes. Milk powder production involves many thermal processes, resulting in a high energy consumption. Over the past decades the current production process has been optimized to a large extent. In order to make the next step forward in energy reduction, as well as in environmental impact, it is necessary to redesign the milk powder production chain. Introduction of new technologies will be the key. Membrane distillation is investigated as an alternative to evaporation for the concentration of milk. Furthermore, a closed loop spray drying system is proposed, aiming to reuse the latent and sensible heat of the exhaust air. Optimization of single process units has an influence on up- and downstream process units. Therefore, it is important to take the whole production chain into account. By combining existing and innovative technologies, evaluating them on energy usage, LCA aspects, and economic aspects different processing chains are optimised. The result is an improved milk powder production chain.</p

Tracing technological development trajectories: A genetic knowledge persistence-based main path approach

The aim of this paper is to propose a new method to identify main paths in a technological domain using patent citations. Previous approaches for using main path analysis have greatly improved our understanding of actual technological trajectories but nonetheless have some limitations. They have high potential to miss some dominant patents from the identified main paths; nonetheless, the high network complexity of their main paths makes qualitative tracing of trajectories problematic. The proposed method searches backward and forward paths from the high-persistence patents which are identified based on a standard genetic knowledge persistence algorithm. We tested the new method by applying it to the desalination and the solar photovoltaic domains and compared the results to output from the same domains using a prior method. The empirical results show that the proposed method overcomes the aforementioned drawbacks defining main paths that are almost 10x less complex while containing more of the relevant important knowledge than the main path networks defined by the existing method.Comment: 20 pages, 7 figure