Water quality prediction based on Naïve Bayes algorithm

In the fast-changing world with increased water demand, water pollution, environmental problems, and related data, information on water quality and suitability for any purpose should be prompt and reliable. Traditional approaches often fail in the attempt to predict water quality classes and new ones are needed to handle a large amount or missing data to predict water quality in real-time. One of such approaches is machine-learning (ML) based prediction. This paper presents the results of the application of the Naïve Bayes, a widely used ML method, in creating the prediction model. The proposed model is based on nine water quality parameters: temperature, pH value, electrical conductivity, oxygen saturation, biological oxygen demand, suspended solids, nitrogen oxides, orthophosphates, and ammonium. It is created in software Netica and tested and verified using the data covering the period 2013–2019 from five locations in Vojvodina Province, Serbia. Forty-eight samples are used to train the model. Once trained, the Naïve Bayes model correctly predicted the class of water sample in 64 out of 68 cases, including cases with missing data. This recommends it as a trustful tool in the transition from traditional to digital water management.

Research on application of ReliefF and improved RVM in water quality grade evaluation

ReliefF algorithm was used to analyze the weight of each water quality evaluation factor, and then based on the Relevance Vector Machine (RVM), Particle Swarm Optimization (PSO) was used to optimize the kernel width factor and hyperparameters of RVM to build a water quality evaluation model, and the experimental results of RVM, PSO-RVM, ReliefF-RVM and PSO-ReliefF-RVM were compared. The results show that ReliefF algorithm, combined with threshold value, selects 5 evaluation factors with significant weight from 8 evaluation factors, which reduces the amount of data used in the model, CSI index is used to calculate the separability of each evaluation factor combination. The results show that the overall separability of the combination is best when the evaluation factor with significant weight is reserved. When different water quality evaluation factors were included, the evaluation accuracy of PSO-ReliefF-RVM model reached 95.74%, 14.23% higher than that of RVM model, which verified the effectiveness of PSO algorithm and ReliefF algorithm, and had a higher guiding significance for the study of water quality grade evaluation. It has good practical application value.

Characterizing dissolved organic matter in Taihu Lake with PARAFAC and SOM method

The three-dimensional fluorescence spectrum has a significant amount of information than the single-stage scanning fluorescence spectrum. At the same time, the parallel factor (PARAFAC) analysis and neural network method can help explore the fluorescence characteristics further, thus could be used to analyse multiple sets of three-dimensional matrix data. In this study, the PARAFAC analysis and the self-organizing mapping (SOM) neural network method are firstly introduced comprehensively. They are then adopted to extract information of the three-dimensional fluorescence spectrum data set for fluorescence characteristics analysis of dissolved organic matter (DOM) in Taihu Lake water. Forty water samples with DOM species were taken from different seasons with the fluorescence information obtained through the three-dimensional fluorescence spectrum analysis, PARAFAC analysis and SOM analysis. The PARAFAC analysis results indicated that the main fluorescence components of dissolved organic matter in Taihu Lake water were aromatic proteins, fulvic acids, and dissolved microorganisms. While the SOM analysis results exhibited that the fluorescence characteristics of the dissolved organics in Taihu Lake varied seasonally. Therefore, the combined method of the three-dimensional fluorescence spectrum analysis, PARAFAC and SOM analysis can provide important information for the characterization of the fluorescence properties of dissolved organic matter in surface water bodies.

Solidification performances of contaminants by red mud-based cementitious paste filling material and leaching behavior of contaminants in different pH and redox potential environments

Considering the urgent need for disposal of red mud and the comprehensive treatment of coal mined-out areas, this paper presented red mud-based cementitious paste filling material (RMFM) to achieve the purpose of green filling treatment. However, the solidification performance of alkaline RMFM for contaminants can be affected when in contact with acid goaf water in practice, which may in turn causes secondary pollution to the surroundings. The leaching tests of RMFM under different pH and redox potential (Eh) conditions were designed to investigate the effects of environmental elements on the solidification performance of RMFM, and primarily investigated the treatment effectiveness of RMFM on goaf water. The test results manifest that the acidic and oxidizing environments could damage the hydration products generated by alkali and sulfate activation, thus affecting the solidification performance, while the alkaline and reducing environments could effectively prevent the release of the contaminants by enhancing the degree of alkali activation and inhibiting oxidation acid forming process. In the possible exposure environment, RMFM could effectively stabilize its own pollutants without secondary pollution. In addition, the powder RMFM samples had significant removal effects on heavy metals, the values of Cu, Pb, and As removal efficiency all reached more than 96.15%.

Spatiotemporal modeling of water inrush spreading in mine roadway networks

Water inrush accidents seriously threaten underground mining production, so the accurate prediction of the spreading process of water inrush is essential for the formulation of water-inrush-control plans and rescue schemes. This paper proposes a spatiotemporal model based on pipe-flow theory to simulate the spreading process of water inrush in mine roadway networks. The energy-loss term is added to this model to improve the simulation accuracy in bifurcated roadways, and pumps and water-blocking equipment are considered in controlling the spreading process of water inrush. Through experimental case studies, the simulation results and the function of the energy-loss term are verified. A sensitivity analysis is then carried out to assess the impact of the model parameters. The results show that the model outputs are most sensitive to the roadway length, cross-section width, and energy-loss coefficient. The model exhibited maximal sensitivity to the geometric parameters compared with the hydraulic parameters. Furthermore, the spreading process of a real water inrush in a coal mine in North China is simulated, and the water-inrush-control measures are evaluated. The overall results indicate that the proposed spatiotemporal model accurately predicts the spreading process of water inrush and is thus applicable to large-scale mine roadway networks.

Antibacterial effect of TiO2 modified with poly-amidoamine dendrimer – G3 on S. aureus and E. coli in aqueous solutions

This study investigated the bacterial removal using TiO2 nanoparticles (NPs) modified with poly-amidoamine dendrimer macromolecule (PAMAM, G3). The PAMAM G3/TiO2 (nanohybrid) was used to specify antibacterial properties via broth microdilution (MBC-Minimum Bactericidal Concentration and MIC-Minimum Inhibitory Concentration- determination), paper disc diffusion, and surface plate count methods. The nanohybrid was characterized via the different techniques. The effects of different factors including initial bacteria count, run time, solution pH, and the nanohybrid concentration were studied. The nanohybrid cytotoxicity was studied on AGS and MKN45 cells line by MTT assay. It was revealed that the nanohybrid was effective in intercepting both bacterial strains growth. The MIC value for S. aureus and E. coli were determined to be 4 and 2 μg/mL, respectively. The MBC value for both strains were calculated to be 32 μg/mL. The results showed removal efficiency of 100% for S. aureus and E. coli bacteria in optimum situation. The decrease in cell viability in the dosage of 32 μg/mL after 72 h treatment for AGS and MKN45 cells line were shown to be 6.2 and 4.6%, respectively. The nanohybrid was able to decrease the S. aureus and E. coli count in solution, which meets the drinking water criterions aligned with WHO guidelines.

Nitrogen source recovery from mature leachate via heat extraction technology: An engineering project application

Large pool of ammonia in mature leachate is challenging to treat with a membrane bioreactor system to meet the discharge standard for pollution control of municipal solid waste landfills in China (GB 16889-2008) without external carbon source addition. In this study, an engineering leachate treatment project with a scale of 2,000 m3/d was operated to evaluate the ammonia heat extraction system (AHES), which contains preheat, decomposition, steam-stripping, ammonia recovery, and centrifuge dewatering. The operation results showed that NH3-N concentrations of raw leachate and treated effluent from an ammonia heat extraction system (AHES) were 1,305–2,485 mg/L and 207–541 mg/L, respectively. The ratio of COD/NH3-N increased from 1.40–1.84 to 7.69–28.00. Nitrogen was recovered in the form of NH4HCO3 by the ammonia recovery tower with the introduction of CO2, wherein, the mature leachate can offer 37% CO2 consumption. The unit consumptions of steam and power were 8.0% and 2.66 kWh/m3 respectively, and the total operation cost of AHES was 2.06 USD per cubic leachate. These results confirm that the heat extraction is an efficient and cost-effective technology for the recovery of nitrogen resource from mature leachate.

Develop an integrating coagulation and RO systems to treat highly turbid water using synthesized coagulants

In the present study coagulation process was used as pretreatment for the RO membrane with turbid raw water collected from Bisalpur Dam, Rajasthan, India. To optimize coagulation performance, three kinds of coagulants, namely, Alum (commercially available), synthesized inorganic polymeric coagulant-medium basicity (IPC-M), and inorganic polymeric coagulant-ultra high basicity (IPC-UH) were examined for turbidity removal with varying operating parameters. It was observed that in the optimum pH range of 6–7, the IPC-UH resulted as the best performing coagulant with 0.99 mg/L equivalent Al2O3 dose revealing 2 NTU residual turbidity and residual aluminium of 0.001 mg/L. Moreover, Langelier saturation index and Ryznar stability index values were evaluated at optimum conditions of all the three coagulants proclaiming negligible scaling potential. Furthermore, the coagulant-treated water (100 L) was fed to the RO membrane, and the performance was noted in terms of flux, pressure, and TDS. It was observed that IPC-UH has the lowest reduction in permeate flux of 0.78 L/min/m2 compared to commercially available coagulant alum (0.90 L/min/m2). Also, the increased feed pressure was observed for all the coagulants treated water with the lowest value of 2.3 kg/cm2 for IPC-UH, which was 2.5 kg/cm2 for Alum (commercially available coagulant). Henceforth, integration of coagulation before the RO system resulted in effective pretreatment of turbid water with very minute scaling.

The effect of anaerobic co-fermentation on acidification performance of food waste and cardboard waste

The fermentation system with high solid materials for food waste (FW) is uneven in nutrition and easy to produce volatile acid accumulation, which causes the reaction system to acidify and affects the normal operation of fermentation. This study evaluated the effect of the co-substrate percentages (FW:CB = 9:1, FW:CB = 8:2, FW:CB = 7:3) and the initial total solid contents (12%, 15%, 18%) on the co-fermentation acidification performance of FW and cardboard waste (CB). The maximum methane production was obtained when mono-fermenting FW had high solids contents(1.4 L/kg). The methane production increased and then decreased with the increasing percentages of CB. Under the conditions of FW:CB = 8:2, the maximum methane production could reach 3.4 L/kg. The lower methane production (1.8 ∼ 2.5 L/kg) with high percentages of CB (FW:CB = 7:3) was translated into higher yields of caproic acid (up to 26%), which indicated lower percentages of CB had a stabilization effect due to the higher buffering capacities in co-fermentation. As a result, this study demonstrated new possibilities for using CB percentages to control the production of high added-value biogas in dry co-fermentation of FW.

Recent advances, influencing factors, and future research prospects using photocatalytic process for produced water treatment

Oilfield-produced water is the primary by-product generated during oil and gas extraction operations. Oilfield-produced water is often severely toxic and poses substantial health, safety, and environmental issues; adequate treatment technologies must bring these streams to a quality level. Photocatalysis is a photochemical catalytic reaction that is a highly promising tool for environmental remediation due to its efficiency in mineralizing persistent and potentially toxic contaminants. However, there is limited understanding of its application to treating oilfield-produced water with a complex and highly variable water composition. This review article discusses the mechanisms and current state of heterogeneous photocatalytic systems for oilfield-produced water treatment, highlighting impediments to knowledge transfer, including the feasibility of practical applications and the identification of essential research requirements. Additionally, the effects of significant variables such as catalyst quantity, pH, organic compound concentration, light intensity, and wavelength were discussed in detail. Some solutions are proposed for scientists and engineers interested in advancing the development of industrial-scale photocatalytic water treatment technologies.