A Novel Low-Temperature Thermal Desalination Technology Using Direct-Contact Spray Method

Due to the emerging water crisis, the global desalination capacity has been expanding exponentially in the past few decades, leading to substantial amount of primary energy consumption. Therefore, the exploration of energy-efficient desalination processes and alternative energy sources has been the subject of great research interests. The spray-assisted low-temperature desalination (SLTD) system is a novel method for desalination that enables efficient renewable energy utilization. It works on the direct-contact spray evaporation/condensation mechanism and uses only hollow chambers. The merits include enhanced heat and mass transfer, lower initial and operational costs, and reduced scaling and fouling issues. This chapter presents a study on the SLTD system driven by sensible heat sources. The working principle of the system will be introduced first. Then a thermodynamic analysis will be presented to obtain the freshwater productivity under different design and operational conditions. Additionally, the energy utilization level will be quantified to highlight the energy wastage when operating with sensible heat sources. Afterward, the system configuration will be modified to maximize the utilization of sensible heat sources and promote productivity. Finally economic viability of the modified design will be evaluated

Direct Contact Heat and Mass Exchanger for Heating, Cooling, Humidification, and Dehumidification

A direct-contact heat and mass exchanger (DCHME) has many advantages over a traditional surface-type heat exchanger, including a high heat transfer coefficient, simplicity of design, and low OPEX and CAPEX. DCHME has a capability to exchange of both heat and mass between the two fluids in the same process. Hence, DCHMEs are widely used in numerous applications in various industries, including the air conditioning industry for cooling and dehumidification and heating and humidification. Based on their structure, DCHME can be categorized into two groups; two fluids direct contact (TFDC) exchanger and two direct contacts with one non-contact fluid (TDCONF) exchanger. This study developed a mathematical model for these two types of exchangers by using a discretized volume with distributed lumped-parameters method instead of using the conventional log mean enthalpy difference (LMHD) and NTU-effectiveness method. Thus, this model can reflect both heat and mass transfer behavior in every spatially distributed physical system. The objective of this study is to develop a mathematical model to be used as a tool for designing DCHME and to be applied as a sub-function of the model predictive control system to predict the effectiveness and dependent parameters of DCHME under the different load conditions and its various input parameters

A thermally-driven seawater desalination system: Proof of concept and vision for future sustainability

Since the 1970s, commercial-scale thermally-driven seawater desalination plants have been powered by low-grade energy sources, drawn either with low-pressure bled-steam from steam turbines or the solar renewable energy harvested that are supplied at relatively low temperatures. Despite the increasing trend of seawater reverse osmosis plants, the role of thermal desalination methods (such as multi-stage flashing and multi-effect distillation) in GCC countries is still relevant in the Arabian Gulf, arising from higher salinity, the frequent algae blooms of seawater and their ability to utilize low temperature heat sources. Given the urgent need for lowering both the capital and operating costs of all processes within the desalination industry and better thermodynamic adaptation of low-grade heat input from renewable sources, the present paper addresses the abovementioned issues by investigating the direct contact spray evaporation and condensation (DCSEC) method. A DCSEC system comprises only hollow chambers (devoid of membranes or tubes, minimal use of chemical and maintenance) where vapor generation (flashing) utilizes the enthalpy difference between the sprayed feed seawater and the saturated vapor enthalpy of the vessels. Concomitantly, vapor is condensed with spray droplets of cooler water (potable) in adjacent condenser vessels, employing a simple design concept. We present detailed design and real seawater experiments data of a DCSEC system for the first time. The water production cost is calculated as 0.52/m3, which is one of the lowest figures reported compared to commercial processes presented by Global Water Intelligence

Experimental Performance of Single-Slope Basin Solar Still Coupled with a Humidification–Dehumidification Cycle

Despite their low distillate yield, single-slope basin solar stills incorporate a simple and cheap technique to secure potable water in arid and rural areas away from fresh water resources and the power grid. Nevertheless, recovering a portion of the inevitable thermal losses from the still will significantly contribute to enhancing its daily distillate productivity and thermal performance. In this manuscript, the latent heat of condensation in single-slope basin solar still was partially recovered and utilized as the thermal energy source for an auxiliary humidification–dehumidification (HDH) distillation cycle. The thermal performance of the resultant SS-HDH distiller was experimentally tested side by side with a separate single-slope basin still of the same basin area. The results showed an increase of about 2 L/m2 in the daily distillate production of the SS-HDH distiller over that of the conventional single-slope basin still. In addition, the thermal efficiency of the SS-HDH distiller was 57% greater than that of the conventional single-slope basin still

Experimental Performance of Single-Slope Basin Solar Still Coupled with a Humidification–Dehumidification Cycle

Despite their low distillate yield, single-slope basin solar stills incorporate a simple and cheap technique to secure potable water in arid and rural areas away from fresh water resources and the power grid. Nevertheless, recovering a portion of the inevitable thermal losses from the still will significantly contribute to enhancing its daily distillate productivity and thermal performance. In this manuscript, the latent heat of condensation in single-slope basin solar still was partially recovered and utilized as the thermal energy source for an auxiliary humidification–dehumidification (HDH) distillation cycle. The thermal performance of the resultant SS-HDH distiller was experimentally tested side by side with a separate single-slope basin still of the same basin area. The results showed an increase of about 2 L/m2 in the daily distillate production of the SS-HDH distiller over that of the conventional single-slope basin still. In addition, the thermal efficiency of the SS-HDH distiller was 57% greater than that of the conventional single-slope basin still

Adsorption of Diphenolic Acid from Contaminated Water onto Commercial and Prepared Activated Carbons from Wheat Straw

The fabrication of carbon materials from biomass residues can be a promising economical approach for absorbing various target pollutants from aqueous phase. In the study, the adsorption of diphenolic acid (DPA) is investigated on activated carbons fabricated from wheat straw (ACWS) and commercial-activated carbon cloth (CACC). Adsorption kinetics, isotherms, and operational variables (solution pH and ionic strength) are analyzed for the adsorption capacity of the DPA on both carbons. The results show that the ACWS has a higher surface area (1164 m2/g) and volume of micropores (0.51 cm3/g) than those of the CACC. The second-order kinetics model fitted the experiment data better than the first kinetics models with a lower percentage of deviation. The adsorption capacity of the ACWS (264.90 mg/g) is higher than the CACC (168.19 mg/g) because of the higher surface area and volume of micropores of the ACWS. The adsorption isotherm shows that the adsorption of the DPA on the ACWS and CACC is consistent with the Langmuir and Freundlich isotherm models, respectively. The pH has a significant effect on DPA adsorption onto both carbons. The adsorption process is favored at the acidic pH, but the presence of electrolytes has no effect on the adsorption capacity of both carbons due to the screening effect. Thus, the preparation of activated carbon from wheat straw is an attractive option to recycle the wheat straw to added-value materials that can be used for the removal of such pollutants from aqueous solution. These findings can increase the research knowledge about the management of different straws in a sustainable way to produce activated carbon for different applications

Forecasting the Mechanical Properties of Plastic Concrete Employing Experimental Data Using Machine Learning Algorithms: DT, MLPNN, SVM, and RF

Increased population necessitates an expansion of infrastructure and urbanization, resulting in growth in the construction industry. A rise in population also results in an increased plastic waste, globally. Recycling plastic waste is a global concern. Utilization of plastic waste in concrete can be an optimal solution from recycling perspective in construction industry. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and tensile strengths of plastic concrete. For predicting the strength of concrete produced with plastic waste, this research integrates machine learning algorithms (individual and ensemble techniques), including bagging and adaptive boosting by including weak learners. For predicting the mechanical properties, 80 cylinders for compressive strength and 80 cylinders for split tensile strength were casted and tested with varying percentages of irradiated plastic waste, either as of cement or fine aggregate replacement. In addition, a thorough and reliable database, including 320 compressive strength tests and 320 split tensile strength tests, was generated from existing literature. Individual, bagging and adaptive boosting models of decision tree, multilayer perceptron neural network, and support vector machines were developed and compared with modified learner model of random forest. The results implied that individual model response was enriched by utilizing bagging and boosting learners. A random forest with a modified learner algorithm provided the robust performance of the models with coefficient correlation of 0.932 for compressive strength and 0.86 for split tensile strength with the least errors. Sensitivity analyses showed that tensile strength models were least sensitive to water and coarse aggregates, while cement, silica fume, coarse aggregate, and age have a substantial effect on compressive strength models. To minimize overfitting errors and corroborate the generalized modelling result, a cross-validation K-Fold technique was used. Machine learning algorithms are used to predict mechanical properties of plastic concrete to promote sustainability in construction industry

Predicting the Lateral Load Carrying Capacity of Reinforced Concrete Rectangular Columns: Gene Expression Programming

This research presents a novel approach of artificial intelligence (AI) based gene expression programming (GEP) for predicting the lateral load carrying capacity of RC rectangular columns when subjected to earthquake loading. To achieve the desired research objective, an experimental database assembled by the Pacific Earthquake Engineering Research (PEER) center consisting of 250 cyclic tested samples of RC rectangular columns was employed. Seven input variables of these column samples were utilized to develop the coveted analytical models against the established capacity outputs. The selection of these input variables was based on the linear regression and cosine amplitude method. Based on the GEP modelling results, two analytical models were proposed for computing the flexural and shear capacity of RC rectangular columns. The performance of both these models was evaluated based on the four key fitness indicators, i.e., coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), and root relative squared error (RRSE). From the performance evaluation results of these models, R2, RMSE, MAE, and RRSE were found to be 0.96, 53.41, 38.12, and 0.20, respectively, for the flexural capacity model, and 0.95, 39.47, 28.77, and 0.22, respectively, for the shear capacity model. In addition to these fitness criteria, the performance of the proposed models was also assessed by making a comparison with the American design code of concrete structures ACI 318-19. The ACI model reported R2, RMSE, MAE, and RRSE to be 0.88, 101.86, 51.74, and 0.39, respectively, for flexural capacity, and 0.87, 238.74, 183.66, and 1.35, respectively, for shear capacity outputs. The comparison depicted a better performance and higher accuracy of the proposed models as compared to that of ACI 318-19

Groundwater Quality Assessment for Drinking and Irrigation Purposes at Al-Jouf Area in KSA Using Artificial Neural Network, GIS, and Multivariate Statistical Techniques

Groundwater is an essential resource for drinking and agricultural purposes in the Al-Jouf region, Saudi Arabia. The main objective of this study is to assess groundwater quality for drinking and irrigation purposes in the Al-Jouf region. Physicochemical characteristics of groundwater were determined, including total dissolved solids (TDS), pH, electric conductivity (EC), hardness, and various anions and cations. The groundwater quality index (WQI) was calculated to determine the suitability of groundwater for drinking purposes. The EC, sodium percentage (Na+ %), magnesium hazard (MH), sodium adsorption ratio (SAR), potential salinity (PS), and Kelley’s ratio (KR) were assessed to evaluate the suitability of groundwater for irrigation. Effective statistical tests and Feed-forward neural network (FFNN) modeling were applied to reveal the correlation between parameters and predict WQI. The results indicated that approximately all samples are appropriate for drinking and irrigation uses except samples of the Al Qaryat region. The ionic abundance ranking was Na+ > Ca2+ > Mg2+ > K+ for cations, and Cl− > SO42− > NO3− for anions. Moreover, the groundwater is dominated by alkali metals (K+ and Na+) and controlled by the rock–water interaction process. The indicators of groundwater quality for irrigation and drinking according to the following criteria (Na+ %, SAR, KR, MH, PS, WQI (WHO), and WQI (BIS)) can be predicted by the FFNN with root mean square errors (RMSE) of 0.136, 0.070, 0.022, 0.073, 2.45 × 10−3, 1.45 × 10−2, and 1.18 × 10−2, respectively, and R2 of 0.99, 1.00, 0.99, 0.99, 1.00, 1.00, and 1.00, respectively