Optimizing Sustainability: Exergoenvironmental Analysis of a Multi-Effect Distillation with Thermal Vapor Compression System for Seawater Desalination

International audienceSeawater desalination stands as an increasingly indispensable solution to address global water scarcity issues. This study conducts a thorough exergoenvironmental analysis of a multi-effect distillation with thermal vapor compression (MED-TVC) system, a highly promising desalination technology. The MED-TVC system presents an energy-efficient approach to desalination by harnessing waste heat sources and incorporating thermal vapor compression. The primary objective of this research is to assess the system’s thermodynamic efficiency and environmental impact, considering both energy and exergy aspects. The investigation delves into the intricacies of energy and exergy losses within the MED-TVC process, providing a holistic understanding of its performance. By scrutinizing the distribution and sources of exergy destruction, the study identifies specific areas for enhancement in the system’s design and operation, thereby elevating its overall sustainability. Moreover, the exergoenvironmental analysis quantifies the environmental impact, offering vital insights into the sustainability of seawater desalination technologies. The results underscore the significance of every component in the MED-TVC system for its exergoenvironmental performance. Notably, the thermal vapor compressor emerges as pivotal due to its direct impact on energy efficiency, exergy losses, and the environmental footprint of the process. Consequently, optimizing this particular component becomes imperative for achieving a more sustainable and efficient desalination system

Energy and Exergy Analyses of a PWR-Type Nuclear Power Plant Coupled with an ME-TVC-MED Desalination System

International audienceElectricity–water cogeneration power plants are an important tool for advancing sustainable water treatment technologies because they provide a cost-effective and environmentally friendly solution for meeting the energy and water needs of communities. By integrating power and water production, these technologies can reduce carbon emissions and help mitigate the impact of climate change. This work deals with the energy and exergy analysis of a cogeneration plant for electrical power generation and water desalination using real operational data. The power side is a pressurized water reactor (PWR) nuclear power plant (NPP), while the desalination side is a multi-effect distillation (MED) system with a thermo-vapor compressor (TVC) plant coupled with a conventional multi-effect plant (ME-TVC-MED). A mathematical model was implemented in MATLAB software and validated through a comparison with previously published research. The exergy analysis was carried out based on the second law of thermodynamics to evaluate the irreversibility of the plant and the subsystems. In this study, the components of the sub-systems were analyzed separately to identify and quantify the component that has a high loss of energy and exergy. According to the energy and exergy analyses, the highest source of irreversibility occurs in the reactor core with 50% of the total exergy destruction. However, turbines, steam generators, and condensers also contribute to energy loss. Further, the thermodynamic efficiency of the cogeneration plant was obtained as 35.38%, which is more effective than other systems. In the ME-TVC-MED desalination unit, the main sources of energy losses are located in the evaporators and the thermo-compressor (about 50% and 36%, respectively). Moreover, the exergetic efficiency of the ME-TVC-MED unit was found to be low at 6.43%, indicating a high degree of technical inefficiency in the desalination process. Therefore, many opportunities exist to improve the performance of the cogeneration system. © 2023 by the authors

Characterization and Thermal Evaluation of a Novel Bio-Based Natural Insulation Material from <i>Posidonia oceanica</i> Waste: A Sustainable Solution for Building Insulation in Algeria

Natural bio-based insulation materials have been the most interesting products for good performance and low carbon emissions, becoming widely recognized for their sustainability in the context of climate change and the environmental impact of the building industry. The main objective of this study is to characterize a new bio-sourced insulation material composed of fibers and an adhesive based on cornstarch. This innovative material is developed from waste of the marine plant called Posidonia oceanica (PO), abundantly found along the Algerian coastline. The research aims to valorize this PO waste by using it as raw material to create this novel material. Four samples with different volumetric adhesive fractions (15%, 20%, 25%, and 30%) were prepared and tested. The collected fractions underwent a series of characterizations to evaluate their properties. The key characteristics studied include density, thermal conductivity, and specific heat. The results obtained for the thermal conductivity of the different composites range between 0.052 and 0.067 W.m−1.K−1. In addition, the findings for thermal diffusivity and specific heat are similar to those reported in the scientific literature. However, the capillary absorption of the material is slightly lower, which indicates that the developed bio-sourced material exhibits interesting thermal performance, justifying its suitability for use in building insulation in Algeria

Analysis of Desalination Performance with a Thermal Vapor Compression System

peer reviewedMulti-effect distillation with thermal vapor compression (MED-TVC) is a highly energy-efficient desalination technology that can provide a reliable and sustainable source of high-quality water, particularly in areas with limited energy infrastructure and water resources. In this study, a numerical model based on exergoeconomic approach is developed to analyze the economic performance of a MED-TVC system for seawater desalination. A parallel/cross feed configuration is considered because of its high energy efficiency. In addition, a parametric study is performed to evaluate the effects of some operational parameters on the total water price, such as the top brine temperature, seawater temperature, motive steam flow rate, and number of effects. The obtained results indicate that the total water price is in the range of 1.73 USD/m3 for a distilled water production of 55.20 kg/s. Furthermore, the exergy destructions in the effects account for 45.8% of the total exergy destruction. The MED effects are also identified to be the most relevant component from an exergoeconomic viewpoint. Careful attention should be paid to these components. Of the total cost associated with the effects, 75.1% is due to its high thermodynamic inefficiency. Finally, the parametric study indicates that adjusting the top brine temperature, the cooling seawater temperature, the motive steam flow rate, and the number of effects has a significant impact on the TWP, which varies between 1.42 USD/m3 and 2.85 USD/m3

Numerical Modelling and Performance Evaluation of Vacuum Membrane Distillation for Energy-Efficient Seawater Desalination: Towards Energy-Efficient Solutions

International audienceVacuum membrane distillation (VMD) is a compelling technique for desalinating water because it exhibits superior pure permeability at lower operating temperatures compared to other technologies. This leads reduced energy consumption, heat loss via conduction across the surface, and minimal transfer through due low pressure on permeate side. Detailed modelling of mass in VMD essential optimizing process as provides valuable insights that contribute advancement successful implementation seawater desalination using technology. The aim this study establish comprehensive numerical model describes vapor hydrophobic micro-porous single-stage multi-stage processes desalination. predictions were experimental data addition computations based an existing literature database, good agreement has been found. investigation also conducted sensitivity analysis variables specifications performance, well assessment impact temperature concentration polarization. obtained results showed permeation flux reached 18.42 kg/m2·h 35 g/L feed concentration, 65 °C temperature, 50 L/h flow rate, 3 kPa vacuum pressure. Moreover, findings revealed was most significant factor, while rate least important determining flux. Additionally, suggested effectiveness heavily relies composition support materials. Finally, confirmed polarization had more effect reduction tha

Mixed Coagulant-flocculant Optimization for Pharmaceutical Effluent Pretreatment Using Response Surface Methodology and Gaussian Process Regression

peer reviewedWastewater from the Antibiotical-Saidal pharmaceutical plant (Medéa) was pretreated by coagulation-flocculation using copper sulfate (CuSO4), iron chloride (FeCl3), and mixture of the two salts combined in a 1:1 (v/v) ratio in the present study. Response surface methodology (RSM) was used to optimize pH and coagulant dosage as independent variables, while dissolved organic carbon (DOC), absorbance at 254 nm (UV 254), and turbidity were provided as dependent variables in the central composite design (CCD). Then, the databases of the three treatments were combined in a single database to create a general model valid for the three treatments at the same time, and to predict the reduction rates of DOC, UV254, and turbidity, using the Gaussian process regression coupled with the dragonfly optimization algorithm (GPR-DA). To have the best model obtained between RMS and GPR-DA, an experimental validation was carried out after having had the optimal conditions of each type of coagulant, using the multi-objective optimization technique. The results of the experimental validation show the superiority of the GPR-DA model compared to the RSM model. Also, the results show that the mixed coagulant (CuSO4+ FeCl3) obtain better results than CuSO4 or FeCl3 alone with a treatment efficiency equal to 92.68% at pH = 5 and dosage = 600 mg/L, and the reductions in DOC, UV 254 and turbidity are 97.32%, 82.90% and 96.47%, respectively

Modeling and Optimization of Hybrid Fenton and Ultrasound Process for Crystal Violet Degradation Using AI Techniques

International audienceThis study conducts a comprehensive investigation to optimize the degradation of crystal violet (CV) dye using the Fenton process. The main objective is to improve the efficiency of the Fenton process by optimizing various physicochemical factors such as the Fe2+ concentration, H2O2 concentration, and pH of the solution. The results obtained show that the optimal dosages of Fe2+ and H2O2 giving a maximum CV degradation (99%) are 0.2 and 3.13 mM, respectively. The optimal solution pH for CV degradation is 3. The investigation of the type of acid for pH adjustment revealed that sulfuric acid is the most effective one, providing 100% yield, followed by phosphoric acid, hydrochloric acid, and nitric acid. Furthermore, the examination of sulfuric acid concentration shows that an optimal concentration of 0.1 M is the most effective for CV degradation. On the other hand, an increase in the initial concentration of the dye leads to a reduction in the hydroxyl radicals formed (HO•), which negatively impacts CV degradation. A concentration of 10 mg/L of CV gives complete degradation of dye within 30 min following the reaction. Increasing the solution temperature and stirring speed have a negative effect on dye degradation. Moreover, the combination of ultrasound with the Fenton process resulted in a slight enhancement in the CV degradation, with an optimal stirring speed of 300 rpm. Notably, the study incorporates the use of Gaussian process regression (GPR) modeling in conjunction with the Improved Grey Wolf Optimization (IGWO) algorithm to accurately predict the optimal degradation conditions. This research, through its rigorous investigation and advanced modeling techniques, offers invaluable insights and guidelines for optimizing the Fenton process in the context of CV degradation, thereby achieving the twin goals of cost reduction and environmental impact minimization

Energy and Exergy Analysis of Solar Air Gap Membrane Distillation System for Seawater Desalination

International audienceAir gap membrane distillation (AGMD) is a widely utilized technology for producing drinking water due to its low heat loss, high thermal efficiency, and compatibility with solar energy. The application of the first and second laws of thermodynamics in energy and exergy analyses provides a comprehensive evaluation of the efficiency of thermal processes. This study aims to examine numerically the energy and exergy performance indicators of a solar AGMD system used for seawater desalination. The simulation was carried out using MATLAB 9.7 software. The total thermal efficiency and overall efficiency of each element in the AGMD system were calculated for various solar field energy outputs, and moreover, a parametric study was conducted. The results indicate that the exergetic efficiency of the AGMD system components was the lowest in the solar field, with the concentrator having the lowest energy efficiency. Additionally, the thermal and exergetic efficiency of the entire solar AGMD system decreases along with the raise of ambient temperature. An additional investigation was conducted to better apprehend the sources of exergy destruction in the solar field. The obtained results from this study can be employed as a guide to reduce exergy destruction in the whole solar AGMD desalination system with recognition of the main sources of irreversibility

Advancing Water Quality Research: K-Nearest Neighbor Coupled with the Improved Grey Wolf Optimizer Algorithm Model Unveils New Possibilities for Dry Residue Prediction

International audienceMonitoring stations have been established to combat water pollution, improve the ecosystem, promote human health, and facilitate drinking water production. However, continuous and extensive monitoring of water is costly and time-consuming, resulting in limited datasets and hindering water management research. This study focuses on developing an optimized K-nearest neighbor (KNN) model using the improved grey wolf optimization (I-GWO) algorithm to predict dry residue quantities. The model incorporates 20 physical and chemical parameters derived from a dataset of 400 samples. Cross-validation is employed to assess model performance, optimize parameters, and mitigate the risk of overfitting. Four folds are created, and each fold is optimized using 11 distance metrics and their corresponding weighting functions to determine the best model configuration. Among the evaluated models, the Jaccard distance metric with inverse squared weighting function consistently demonstrates the best performance in terms of statistical errors and coefficients for each fold. By averaging predictions from the models in the four folds, an estimation of the overall model performance is obtained. The resulting model exhibits high efficiency, with remarkably low errors reflected in the values of R, R2, R2ADJ, RMSE, and EPM, which are reported as 0.9979, 0.9958, 0.9956, 41.2639, and 3.1061, respectively. This study reveals a compelling non-linear correlation between physico-chemical water attributes and the content of dry tailings, indicating the ability to accurately predict dry tailing quantities. By employing the proposed methodology to enhance water quality models, it becomes possible to overcome limitations in water quality management and significantly improve the precision of predictions regarding critical water parameters. © 2023 by the authors