Ionic resource recovery for carbon neutral papermaking wastewater reclamation by a chemical self-sufficiency zero liquid discharge system

Multiobjective Optimization of Papermaking Wastewater Treatment Processes under Economic, Energy, and Environmental Goals

Due to the heterogeneity of recycled paper materials and the production conditions, pollutants in papermaking wastewater fluctuate sharply over time. Quality control of the papermaking wastewater treatment process (PWTP) is challenging and costly. As regulations are also growing about the environmental effects of the PWTP on greenhouse gas (GHG) emission, energy consumption, etc., the PWTP formulates a complex multiobjective optimization problem. This research established a multiagent deep reinforcement learning framework to simultaneously optimize process cost, energy consumption, and GHG emission in the PWTP, subjected to the effluent quality, to realize economic, energy, and environmental (3E) goals. The biological treatment process of wastewater in paper mills was simulated using benchmark simulation model no. 1 (BSM1). The data generated based on the BSM manual was utilized for model training, and real data acquired from a local papermaking factory was used to estimate the model performance. The results show that the proposed method outperforms conventional techniques in identifying the best control strategies for multiple targets.

Bipolar membrane electrodialysis integrated with in-situ CO2 absorption for simulated seawater concentrate utilization, carbon storage and production of sodium carbonate

In the context of carbon capture, utilization, and storage, the high-value utilization of carbon storage presents a significant challenge. To address this challenge, this study employed the bipolar membrane electrodialysis integrated with carbon utilization technology to prepare Na2CO3 products using simulated seawater concentrate, achieving simultaneous saline wastewater utilization, carbon storage and high-value production of Na2CO3. The effects of various factors, including concentration of simulated seawater concentrate, current density, CO2 aeration rate, and circulating flow rate of alkali chamber, on the quality of Na2CO3 product, carbon sequestration rate, and energy consumption were investigated. Under the optimal condition, the CO32- concentration in the alkaline chamber reached a maximum of 0.817 mol/L with 98 mol% purity. The resulting carbon fixation rate was 70.50%, with energy consumption for carbon sequestration and product production of 5.7 kWhr/m3 CO2 and 1237.8 kWhr/ton Na2CO3, respectively. This coupling design provides a triple-win outcome promoting waste reduction and efficient utilization of resources.

Application of machine learning models to improve the prediction of pesticide photodegradation in water by ZnO-based photocatalysts

Pesticide pollution has been posing a significant risk to human and ecosystems, and photocatalysis is widely applied for the degradation of pesticides. Machine learning (ML) emerges as a powerful method for modeling complex water treatment processes. For the first time, this study developed novel ML models that improved the estimation of the photocatalytic degradation of various pesticides using ZnO-based photocatalysts. The input parameters encompassed the source of light, mass proportion of dopants to Zn, initial pesticide concentration (C0), pH of the solution, catalyst dosage and irradiation time. Additionally, physicochemical properties such as the molecular weight of the dopants and pesticides, as well as the water solubility of both dopants and pesticides, were considered. Notably, the numerical data were extracted from the literature via relevant tables (directly) or graphs (indirectly) using the web-based tool WebPlotDigitizer. Four ML models including multi-layer perceptron artificial neural network (MLP-ANN), particle swarm optimization-adaptive neuro fuzzy inference system (PSO-ANFIS), radial basis function (RBF), and coupled simulated annealing-least squares support vector machine (CSA-LSSVM) were developed. In comparison, RBF showed the best accuracy of modeling among all models, with the highest determination coefficient (R2) of 0.978 and average absolute relative deviation (AARD) of 4.80%. RBF model was effective in estimating the photocatalytic degradation of pesticides except for 2-chlorophenol, triclopyr and lambda-cyhalothrin, where CSA-LSSVM model demonstrated superior performance. Dichlorvos was completely degraded by ZnO photocatalyst under visible light. The sensitivity analysis by relevancy factor exhibited that light irradiation time and initial pesticide concentration were the most important parameters influencing photocatalytic degradation of pesticides positively and negatively, respectively. The new ML models provide a powerful tool for predicting pesticide degradation in wastewater treatment, which will reduce photochemical experiments and promote sustainable development.

Contrasting mixed scaling patterns and mechanisms of nanofiltration and membrane distillation

Oriented towards the pressing needs for hypersaline wastewater desalination and zero liquid discharge (ZLD), the contrasting mixed scaling of thermal-driven vacuum membrane distillation (VMD) and pressure-driven nanofiltration (NF) were investigated in this work. Bulk crystallization was the main mechanism in VMD due to the high salinity and temperature, but the time-independent resistance by the adsorption of silicate and organic matter dominated the initial scaling process. Surface crystallization and the consequent pore-blocking were the main scaling mechanisms in NF, with the high permeate drag force, hydraulic pressure, and cross-flow rate resulting in the dense scaling layer mainly composed of magnesium-silica hydrate (MSH). Silicate enhanced NF scaling with a 75% higher initial flux decline rate attributed to the MSH formation and compression, but delayed bulk crystallization in VMD. Organic matter presented an anti-scaling effect by delaying bulk crystallization in both VMD and NF, but specifically promoted CaCO3 scaling in NF. Furthermore, the incipient scaling was intensified as silicate and organic matter coexisted. The scaling mechanism shifted from surface to bulk crystallization due to the membrane concentration in both VMD and NF. This work fills the research gaps on mixed scaling mechanisms in different membrane processes, which offers insights for scaling mitigation and thereby supports the application of ZLD.

Comparative investigations on the incorporation of biogenic Fe products into anaerobic granular sludge of different sources: Fe loading capacity, physicochemical properties, microbial community and long-term methanogenesis performance

Anaerobic granular sludge (AGS) has been regarded as the core of lots of advanced anaerobic reactors. Formation of biogenic Fe products and their incorporation into AGS could influence interspecies electron transfer and methanogenesis performance. In this study, with anaerobic granular sludge (AGS) from different sources (brewery, chemical plant, paper mill, citric acid factory, and food factory) as the research targets, the formation of biogenic iron products in AGS through the biologically induced mineralization process was studied. Furthermore, the influences of physicochemical properties and microbial community on methanogenesis were investigated. Results showed that all the AGS of different sources possessed the capacity to form biogenic Fe products through dissimilatory iron-reduction process, and diverse Fe minerals including magnetite (Fe3O4), hematite (Fe2O3), goethite (FeOOH), siderite (FeCO3) and wustite (FeO) were incorporated into AGS. The AGS loaded with Fe minerals (Fe-AGS) showed increased conductivity, magnetism and zeta-potential comparing to the control. Those Fe-AGS of different sources demonstrated different methanogenesis performance during the long-term operation (50 days). Methane production was increased for the Fe-AGS of citric acid (6.99-32.50%), food (8.33-37.46%), chemical (2.81-7.22%) and brewery plants (2.27-2.81%), but decreased for the Fe-AGS of paper mill (54.81-72.2%). The changes of microbial community and microbial correlations in AGS as a response to Fe minerals incorporation were investigated. For the Fe-AGS samples with enhanced methane production capability, it was widely to find the enriched populations of fermentative and dissimilatory iron reducing bacteria Clostridium_sensu_stricto_6, Bacteroidetes_vadinHA17 and acetoclastic methanogens Methanosaeta, and positive correlations between them. This study provides comprehensive understanding on the effects of incorporation biogenic Fe products on AGS from different sources.

Mixed scaling patterns and mechanisms of high-pressure nanofiltration in hypersaline wastewater desalination

Nanofiltration (NF) will play a crucial role in salt fractionation and recovery, but the complicated and severe mixed scaling is not yet fully understood. In this work, the mixed scaling patterns and mechanisms of high-pressure NF in zero-liquid discharge (ZLD) scenarios were investigated by disclosing the role of key foulants. The bulk crystallization of CaSO4 and Mg-Si complexes and the resultant pore blocking and cake formation under high pressure were the main scaling mechanisms in hypersaline desalination. The incipient scalants were Mg-Si hydrates, CaF2, CaCO3, and CaMg(CO3)2. Si deposited by adsorption and polymerization prior to and impeded Ca scaling when Mg was not added, thus pore blocking was the main mechanism. The amorphous Mg-Si hydrates contribute to dense cake formation under high hydraulic pressure and permeate drag force, causing rapid flux decline as Mg was added. Humic acid has a high affinity to Ca2+by complexation, which enhances incipient scaling by adsorption or lowers the energy barrier of nucleation but improves the interconnectivity of the foulants layer and inhibits bulk crystallization due to the chelation and directional adsorption. Bovine serum albumin promotes cake formation due to the low electrostatic repulsion and acts as a cement to particles by adsorption and bridging in bulk. This work fills the research gaps in mixed scaling of NF, which is believed to support the application of ZLD and shed light on scaling in hypersaline/ultra-hypersaline wastewater desalination applications.

Sustainable wastewater treatment by RO and hybrid organic polyamide membrane nanofiltration system for clean environment

One of the environmental pollution is happened by the discharge of industrial wastewater that needs to be adequately filtered. Given that the effluent from the leather industry contains high levels of chromium, heavy metals, lipids, and Sulphur, it is one of the wastewater disposals that are most damaging. This experimental study focuses on reverse osmosis and hybrid organic polyimide membrane for nanofiltration for sustainable wastewater treatment. In the RO and organic polyamide Nano-porous membranes, a thin film of polyamide membrane was used for efficient filtration. Taguchi analysis optimized process parameters such as pressure, temperature, pH, and volume reduction factor. The outcome shows an 89% reduction in total wastewater hardness, an 88% reduction in sulfate, and an 89% efficiency reduction in COD. As a result, the proposed technology significantly increased filtration efficiency.