Integration of machine learning-based prediction for enhanced Model's generalization: Application in photocatalytic polishing of palm oil mill effluent (POME)

Optimized photodegradation of palm oil agroindustry waste effluent using multivalent manganese-modified black titanium dioxide

This article presents a methodological approach to use manganese (Mn³⁺Mn⁷⁺)-modified black titanium dioxide (Mn/BTiO₂) as a photocatalyst to optimize and improve visible-light-driven photodegradation of treated agro-industrial effluent (TPOME). A modified wet chemical process was used to prepare BTiO₂. The BTiO₂ was then wet impregnated with Mn and calcined at 300 °C for 1 h to produce Mn/BTiO₂. The activity of Mn/BTiO₂ was investigated in terms of photo-assisted elimination of chemical oxygen demand (COD), phenolic compounds (PCs), color, and total organic carbon (TOC). Using the design of experiments (DOE), the conditions of the photocatalytic process, including photocatalyst loading, Mn concentration, hydrogen peroxide (H₂O₂) dose, and irradiation time, were optimized. Under the optimum conditions (0.85 g/L photocatalyst loading, 0.048 mol/L H₂O₂ dose, 0.301 wt.% Mn concentration, and 204 min irradiation time) COD, PCs, color, and TOC removal efficiencies of 88.87%, 86.04%, 62.8%, and 84.66%, respectively, were obtained. Statistical analysis showed that the response variable's removal from TPOME estimation had high R² and low RMSE, MSE, MAD, MAE, and MAPE values, indicating high reliability. This study demonstrated the significant potential of the developed photocatalytic system for the treatment of waste effluent generated by the palm oil industry and other agro-industries, with the ability to simultaneously reduce a number of organic pollution indicators (OPIs).

Machine learning integrated photocatalysis: progress and challenges

Discovering efficient photocatalysts has long been the goal of photocatalysis, which has traditionally been driven by serendipitous or try-and-error strategies. Recent developments in photocatalysis integrated with machine learning techniques promise to accelerate the discovery of photocatalysts, but are also facing significant challenges. In this review, advances in machine learning integrated photocatalysis are first presented from the perspective of three main photocatalytic processes: light harvesting, charge generation and separation, and surface redox reactions. Next, progress in using machine learning to understand complex photoactivity-structure relationships and identify the factors governing activity follows. A future photocatalysis paradigm is then provided with the integration of artificial intelligence, robots and automation. Lastly, we discuss the current challenges in machine learning integrated photocatalysis. This review aims to provide a systematic overview and guidelines to the broad scientific community interested in photocatalysis and artificial intelligence for solar fuel synthesis.

Web-Based Data to Quantify Meteorological and Geographical Effects on Heat Stroke: Case Study in China

Heat stroke is a serious heat-related health outcome that can eventually lead to death. Due to the poor accessibility of heat stroke data, the large-scale relationship between heat stroke and meteorological factors is still unclear. This work aims to clarify the potential relationship between meteorological variables and heat stroke, and quantify the meteorological threshold that affected the severity of heat stroke. We collected daily heat stroke search index (HSSI) and meteorological data for the period 2013-2020 in 333 Chinese cities to analyze the relationship between meteorological variables and HSSI using correlation analysis and Random forest (RF) model. Temperature and relative humidity (RH) accounted for 62% and 9% of the changes of HSSI, respectively. In China, cases of heat stroke may start to occur when temperature exceeds 36°C and RH exceeds 58%. This threshold was 34.5°C and 79% in the north of China, and 36°C and 48% in the south of China. Compared to RH, the threshold of temperature showed a more evident difference affected by altitude and distance from the ocean, which was 35.5°C in inland cities and 36.5°C in coastal cities; 35.5°C in high-altitude cities and 36°C in low-altitude cities. Our findings provide a possible way to analyze the interaction effect of meteorological variables on heat-related illnesses, and emphasizes the effects of geographical environment. The meteorological threshold quantified in this research can also support policymaker to establish a better meteorological warning system for public health.

Modeling defluoridation of real-life groundwater by a green adsorbent aluminum/olivine composite: Isotherm, kinetics, thermodynamics and novel framework based on artificial neural network and support vector machine

The kinetic, isotherm, and thermodynamics of adsorptive removal of fluoride from the real-life groundwater was evaluated to assess the applicability of a green adsorbent, aluminum/olivine composite (AOC). The isotherm and kinetics were demonstrated by the Freundlich and Elovich model indicating significant surface heterogeneity of AOC in favouring the fluoride sorption. The fluoride removal efficiency of AOC was achieved as 87.5% after 240 min of contact time. The diffusion kinetic model exhibited that both the intra-particle and film diffusion together control the rate-limiting step of fluoride adsorption. A negative value of ΔG⁰ (-19.919 kJ/mol) at 303 K confirmed the spontaneous adsorption reaction of fluoride, and its endothermic nature was supported by the negative value of ΔH⁰ (39.504 kJ/mol). A novel framework for a predictive model by artificial neural network (ANN), and support vector machine (SVM) considering the real and synthetic fluoride-containing water was developed to assess the efficiency of adsorbent under different scenarios. ANN model was observed to be statistically significant (RMSE: 1.0955 and R²: 0.9982) and the proposed method may be instrumental in a similar area for benchmarking the synthetic and real-life samples. The low desorption potential of the spent adsorbent exhibited safe disposal of sludge and the secondary-pollutant-free treated water by the efficient and green adsorbent AOC enhanced the field-scale applicability of the green technology.

Advances in pretreatment technology for handling the palm oil mill effluent: Challenges and prospects

The palm oil mill effluent (POME) from palm milling oil activities is discharged into various water bodies which poses several environmental problems including turbidity, increases COD and BOD, adds oil and grease, increases total nitrogen, and other pollutants. Therefore, it requires effective treatment to remove the pollutants before disposal. The objective was to critically discuss the performance of POME pretreatments along with their limitations. To offer a coverage on the present less efficient technologies, the opportunities and challenges of advanced pretreatments that combine magnetic materials and natural composites as adsorbents are comprehensively reviewed here. Moreover, potential of various magnetic materials for POME pretreatment has been described. Several existing pretreatment methods such as physical pretreatments, chemical pretreatments, coagulation-flocculation, and adsorption can remove pollutant content from POME with certain limitations and the use of magnetic composite adsorbents can enhance the treatment efficiency.

Synthesis and Characterization of Manganese-Modified Black TiO2 Nanoparticles and Their Performance Evaluation for the Photodegradation of Phenolic Compounds from Wastewater

The release of phenolic-contaminated treated palm oil mill effluent (TPOME) poses a severe threat to human and environmental health. In this work, manganese-modified black TiO₂ (Mn-B-TiO₂) was produced for the photodegradation of high concentrations of total phenolic compounds from TPOME. A modified glycerol-assisted technique was used to synthesize visible-light-sensitive black TiO₂ nanoparticles (NPs), which were then calcined at 300 °C for 60 min for conversion to anatase crystalline phase. The black TiO₂ was further modified with manganese by utilizing a wet impregnation technique. Visible light absorption, charge carrier separation, and electron–hole pair recombination suppression were all improved when the band structure of TiO₂ was tuned by producing Ti³⁺ defect states. As a result of the enhanced optical and electrical characteristics of black TiO₂ NPs, phenolic compounds were removed from TPOME at a rate of 48.17%, which is 2.6 times higher than P25 (18%). When Mn was added to black TiO₂ NPs, the Ti ion in the TiO₂ lattice was replaced by Mn, causing a large redshift of the optical absorption edges and enhanced photodegradation of phenolic compounds from TPOME. The photodegradation efficiency of phenolic compounds by Mn-B-TiO₂ improved to 60.12% from 48.17% at 0.3 wt% Mn doping concentration. The removal efficiency of phenolic compounds from TPOME diminished when Mn doping exceeded the optimum threshold (0.3 wt%). According to the findings, Mn-modified black TiO₂ NPs are the most effective, as they combine the advantages of both black TiO₂ and Mn doping.

Synthesis and Characterization of Carbon and Carbon-Nitrogen Doped Black TiO2 Nanomaterials and Their Application in Sonophotocatalytic Remediation of Treated Agro-Industrial Wastewater

The conventional open ponding system employed for palm oil mill agro-effluent (POME) treatment fails to lower the levels of organic pollutants to the mandatory standard discharge limits. In this work, carbon doped black TiO₂ (CB-TiO₂) and carbon-nitrogen co-doped black TiO₂ (CNB-TiO₂) were synthesized via glycerol assisted sol-gel techniques and employed for the remediation of treated palm oil mill effluent (TPOME). Both the samples were anatase phase, with a crystallite size of 11.09–22.18 nm, lower bandgap of 2.06–2.63 eV, superior visible light absorption ability, and a high surface area of 239.99–347.26 m²/g. The performance of CNB-TiO₂ was higher (51.48%) compared to only (45.72%) CB-TiO₂. Thus, the CNB-TiO₂ is employed in sonophotocatalytic reactions. Sonophotocatalytic process based on CNB-TiO₂, assisted by hydrogen peroxide (H₂O₂), and operated at an ultrasonication (US) frequency of 30 kHz and 40 W power under visible light irradiation proved to be the most efficient for chemical oxygen demand (COD) removal. More than 90% of COD was removed within 60 min of sonophotocatalytic reaction, producing the effluent with the COD concentration well below the stipulated permissible limit of 50 mg/L. The electrical energy required per order of magnitude was estimated to be only 177.59 kWh/m³, indicating extreme viability of the proposed process for the remediation of TPOME.