A review on the application of biochar as an innovative and sustainable biocarrier material in moving bed biofilm reactors for dye removal from environmental matrices.

Dye decolorization through biological treatment techniques has been gaining momentum as it is based on suspended and attached growth biomass in both batch and continuous modes. Hence, this review focused on the contribution of moving bed biofilm reactors (MBBR) in dye removal. MBBR have been demonstrated to be an excellent technology for pollution extraction, load shock resistance, and equipment size and energy consumption reduction. The review went further to highlight different biocarrier materials for biofilm development this review identified biochar as an innovative and environmentally friendly material produced through the application of different kinds of reusable or recyclable wastes and biowastes. Biochar as a carbonized waste biomass could be a better competitor and environmentally friendly substitute to activated carbon given its lower mass costs. Biochar can be easily produced particularly in rural locations where there is an abundance of biomass-based trash. Given that circular bioeconomy lowers dependency on natural resources by turning organic wastes into an array of useful products, biochar empowers the creation of competitive goods. Thus, biochar was identified as a novel, cost-effective, and long-term management strategy since it brings about several essential benefits, including food security, climate change mitigation, biodiversity preservation, and sustainability improvement. This review concludes that integrating two treatment methods could greatly lead to better color, organic matter, and nutrients removal than a single biological MBBR treatment process.

Predicting micropollutant removal through nanopore-sized membranes using several machine-learning approaches based on feature engineering.

Predicting the efficacy of micropollutant separation through functionalized membranes is an arduous endeavor. The challenge stems from the complex interactions between the physicochemical properties of the micropollutants and the basic principles underlying membrane filtration. This study aimed to compare the effectiveness of a modest dataset on various machine learning tools (ML) tools in predicting micropollutant removal efficiency for functionalized reverse osmosis (RO) and nanofiltration (NF) membranes. The inherent attributes of both the micropollutants and the membranes are utilized as input factors. The chosen ML tools are supervised algorithm (adaptive network-based fuzzy inference system (NF), linear regression framework (linear regression (LR)), stepwise linear regression (SLR) and multivariate linear regression (MVR)), and unsupervised algorithm (support vector machine (SVM) and ensemble boosted tree (BT)). The feature engineering and parametric dependency analysis revealed that characteristics of micropollutants, such as maximum projection diameter (MaxP), minimal projection diameter (MinP), molecular weight (MW), and compound size (CS), exhibited a notably positive impact on the correlation with removal efficiency. Model combination with key variables demonstrated high prediction accuracy in both supervised and unsupervised ML for micropollutant removal efficiency. An NF-grid partitioning (NF-GP) model achieved the highest accuracy with an R 2 value of 0.965, accompanied by low error metrics, specifically an RMSE and MAE of 3.65. It is owed to the handling of the complex spatial and temporal aspects of micropollutant data through division into consistent subsets facilitating improved identification of rejection efficiency and relationships. The inclusion of inputs with both negative and positive correlations introduces variability, amplifies the system responsiveness, and impedes the precision of predictive models. This study identified key micropollutant properties, including MaxP, MinP, MW, and CS, as crucial factors for efficient micropollutant rejection during real-time filtration applications. It also allowed the design of pore size of self-prepared membranes for the enhanced separation of micropollutants from wastewater.

Nickel augmented biochar for sustaining produced water treatment to decarbonize oil and gas industrial waste using anaerobic-aerobic granular cylindrical periodic discontinuous batch reactors.

This study assessed the efficacy of granular cylindrical periodic discontinuous batch reactors (GC-PDBRs) for produced water (PW) treatment by employing eggshell and waste activated sludge (WAS) derived Nickel (Ni) augmented biochar. The synthesized biochar was magnetized to further enhance its contribution towards achieving carbon neutrality due to carbon negative nature, Carbon dioxide (CO2) sorption, and negative priming effects. The GC-PDBR1 and GC-PDBR2 process variables were optimized by the application of central composite design (CCD). This is to maximize the decarbonization rate. Results showed that the systems could reduce total phosphorus (TP) and chemical oxygen demand (COD) by 76-80% and 92-99%, respectively. Optimal organic matter and nutrient removals were achieved at 80% volumetric exchange ratio (VER), 5 min settling time and 3000 mg/L mixed liquor suspended solids (MLSS) concentration with desirability values of 0.811 and 0.954 for GC-PDBR1 and GC-PDBR2, respectively. Employing four distinct models, the biokinetic coefficients of the GC-PDBRs treating PW were calculated. The findings indicated that First order (0.0758-0.5365) and Monod models (0.8652-0.9925) have relatively low R2 values. However, the Grau Second-order model and Modified Stover-Kincannon model have high R2 values. This shows that, the Grau Second Order and Modified Stover-Kincannon models under various VER, settling time, and MLSS circumstances, are more suited to explain the removal of pollutants in the GC-PDBRs. Microbiological evaluation demonstrated that a high VER caused notable rises in the quantity of several microorganisms. Under high biological selective pressure, GC-PDBR2 demonstrated a greater percentage of nitrogen removal via autotrophic denitrification and a greater number of nitrifying bacteria. The overgrowth of bacteria such as Actinobacteriota spp. Bacteroidota spp, Gammaproteobacteria, Desulfuromonas Mesotoga in the phylum, class, and genus, has positively impacted on granule formation and stability. Taken together, our study through the introduction of intermittent aeration GC-PDBR systems with added magnetized waste derived biochar, is an innovative approach for simultaneous aerobic sludge granulation and PW treatment, thereby providing valuable contributions in the journey toward achieving decarbonization, carbon neutrality and sustainable development goals (SDGs).

Experimental Investigation of a Plate-Frame Water Gap Membrane Distillation System for Seawater Desalination.

This study presented a detailed investigation into the performance of a plate-frame water gap membrane distillation (WGMD) system for the desalination of untreated real seawater. One approach to improving the performance of WGMD is through the proper selection of cooling plate material, which plays a vital role in enhancing the gap vapor condensation process. Hence, the influence of different cooling plate materials was examined and discussed. Furthermore, two different hydrophobic micro-porous polymeric membranes of similar mean pore sizes were utilized in the study. The influence of key operating parameters, including the feed water temperature and flow rate, was examined against the system vapor flux and gained output ratio (GOR). In addition, the used membranes were characterized by means of different techniques in terms of surface morphology, liquid entry pressure, water contact angle, pore size distribution, and porosity. Findings revealed that, at all conditions, the PTFE membrane exhibits superior vapor flux and energy efficiency (GOR), with 9.36% to 14.36% higher flux at a 0.6 to 1.2 L/min feed flow rate when compared to the PVDF membrane. The copper plate, which has the highest thermal conductivity, attained the highest vapor flux, while the acrylic plate, which has an extra-low thermal conductivity, recorded the lowest vapor flux. The increasing order of GOR values for different cooling plates is acrylic < HDPE < copper < aluminum < brass < stainless steel. Results also indicated that increasing the feed temperature increases the vapor flux almost exponentially to a maximum flux value of 30.36 kg/m2hr. The system GOR also improves in a decreasing pattern to a maximum value of 0.4049. Moreover, a long-term test showed that the PTFE membrane, which exhibits superior hydrophobicity, registered better salt rejection stability. The use of copper as a cooling plate material for better system performance is recommended, while cooling plate materials with very low thermal conductivities, such as a low thermally conducting polymer, are discouraged.

Energy and Entropy Analyses of a Pilot-Scale Dual Heating HDH Desalination System.

This study focuses on energy and entropy analysis to theoretically investigate the performance of a pilot scale dual heated humidification-dehumidification (HDH) desalination system. Two cases of HDH systems are considered in the analysis. The first case is a dual heated (DH) cycle consisting of 1.59 kW air heater and 1.42 kW water heater with a heat rate ratio of 0.89 (CAOW-DH-I). Whereas the second case is a dual heated HDH cycle comprising of 1.59 kW air heater and 2.82 kW water heater with a heat rate ratio of 1.77 (CAOW-DH-II). As a first step, mathematical code was developed based on heat and mass transfer and entropy generation within the major components of the system. The code was validated against the experimental data obtained from a pilot scale HDH system and was found to be in a good agreement with the experimental results. Theoretical results revealed that there is an optimal mass flowrate ratio at which GOR is maximized, and entropy generation is minimized. Furthermore, the degree of irreversibility within the humidifier component is low and approaches zero, while the specific entropy generation within other components are relatively high and are of the same order of magnitude. Entropy analysis also showed that the dual heated system with heat rate ratio greater than unity is better than the one with heat rate ratio less than unity.