High-Performance Nanocatalyst for Adsorptive and Photo-Assisted Fenton-Like Degradation of Phenol: Modeling Using Artificial Neural Networks

Deep reinforcement learning for automated search of model parameters: photo-fenton wastewater disinfection case study

Numerical optimization solves problems that are analytically intractable at the cost of arriving at a sufficiently good but rarely optimal solution. To maximize the result, optimization algorithms are run with the guidance and supervision of a human, usually an expert in the problem. Recent advances in deep reinforcement learning motivate interest in an artificial agent capable of learning to do the expert’s task. Specifically, we present a proximal policy optimization agent that learns to optimize in a real case study such as the modeling of the photo-fenton disinfection process, which involves a number of parameters that have to be adjusted to minimize the error of the model with respect to the experimental data collected in several trials. The expert spends an average of 4 h to find a suitable set of parameters. On the other hand, the agent we present does not require a human expert to guide or validate the optimization procedure and achieves similar results in $$2.5\times$$ 2.5 × less time.

Efficiency Enhancement in Double-Pass Perforated Glazed Solar Air Heaters with Porous Beds: Taguchi-Artificial Neural Network Optimization and Cost–Benefit Analysis

Analyzing the combination of involving parameters impacting the efficiency of solar air heaters is an attractive research areas. In this study, cost-effective double-pass perforated glazed solar air heaters (SAHs) packed with wire mesh layers (DPGSAHM), and iron wools (DPGSAHI) were fabricated, tested and experimentally enhanced under different operating conditions. Forty-eight iron pieces of wool and fifteen steel wire mesh layers were located between the external plexiglass and internal glass, which is utilized as an absorber plate. The experimental outcomes show that the thermal efficiency enhances as the air mass flow rate increases for the range of 0.014–0.033 kg/s. The highest thermal efficiency gained by utilizing the hybrid optimized DPGSAHM and DPGSAHI was 94 and 97%, respectively. The exergy efficiency and temperature difference (∆T) indicated an inverse relationship with mass flow rate. When the DPGSAHM and DPGSAHI were optimized by the hybrid procedure and employing the Taguchi-artificial neural network, enhancements in the thermal efficiency by 1.25% and in exergy efficiency by 2.4% were delivered. The results show the average cost per kW (USD 0.028) of useful heat gained by the DPGSAHM and DPGSAHI to be relatively higher than some double-pass SAHs reported in the literature.

Hybrid MnFe-LDO-biochar nanopowders for degradation of metronidazole via UV-light-driven photocatalysis: Characterization and mechanism studies

A cost-competitive MnFe-LDO-biochar hybrid catalyst was successfully synthesized via a simple yet efficient technique for the decomposition of metronidazole (MZ). MnFe-LDO-biochar was characterized by various techniques and the results revealed that it has a bandgap of 2.85 eV, high photocurrent response of 3.8 μA cm^-2 and can be separated rapidly from the bulk solution by an external magnet due to its saturation magnetization of 28.5 emu g^-1. Initially, in the dark condition, 20% of MZ was removed after 30 min when 20 mg L^-1 MZ solution was treated with 50 mg MnFe-LDO-biochar in the presence of 6 mM H₂O₂. The MZ degradation increased remarkably to ∼98% upon exposure to a UV light for 60 min. Under various processes, UV/MnFe-LDO-biochar/H₂O₂ presented high degradation rate constant of 0.226 min^-1 and lowest energy consumption cost of 0.38$ at 7.56 kWh m^-3 which is ∼13 times lower than the degradation of MZ by the photolytic process under similar conditions. The MZ photocatalytic decomposition trend revealed a multiprocess mechanism influenced majorly by ^•OH and partly by h⁺ and ^•O₂^-. Note that in MnFe-LDO-biochar/UV system; 5% of MZ degradation was observed after 120 min and reached 13% after 300 min. MnFe-LDO-biochar maintained ∼88% reuse efficiency after three consecutive recycling tests.

CuCr2O4@CaFe-LDO photocatalyst for remarkable removal of COD from high-strength olive mill wastewater

Wastewater from the olive mill constitutes a serious environmental concern, as it is characterized by a high inorganic and organic load. Here, a hybrid photocatalyst based on calcined Ca-Fe-LDH was successfully synthesized for the degradation of phenolic compounds and the removal of chemical oxygen demand (COD) from the high-strength olive mill wastewater (OMW). The catalyst (CuCr₂O₄@CaFe-LDO) displayed a stable ~4.48 µA cm^-2 photocurrent response, a 2.56 eV bandgap and a wide variety of pores with an average size of 12.51 nm. 1.0 g CuCr₂O₄@CaFe-LDO achieved 66% COD removal after 300 mins without an oxidant in the dark, while after 180 mins of reaction, CuCr₂O₄@CaFe-LDO/K₂S₂O₈/sunlight system resulted in ~99% and 98.3% COD and colour removal. Seven phenolic compounds were found in the crude OMW, with hydroxytyrosol (76.84%) and tyrosol (15.14%) being the main ones. The final pH of the sample treated increased from 4.3 to 7.3, which confirmed the degradation of phenolics and fatty acids in the OMW. OH, SO₄^-, h⁺ and O₂^- contributed notably to the degradation of polyphenols and the spent catalyst was easily and rapidly recovered from the bulk solution due to its saturation magnetization of 54.7 emu g^-1.

Response surface methodology and artificial neural network for remediation of acid orange 7 using TiO2-P25: optimization and modeling approach

The primary responsibility for continuously discharging toxic organic pollutants into water bodies and open environments is the increase in industrial and agricultural activities. Developing economical and suitable methods to continuously remove organic pollutants from wastewater is highly essential. The aim of the present research was to apply response surface methodology (RSM) and artificial neural networks (ANNs) for optimization and modeling of photocatalytic degradation of acid orange 7 (AO7) by commercial TiO2-P25 nanoparticles (TNPs). Dose of TNPs, pH, and AO7 concentration were selected as investigated parameters. RSM results reveal the reflective rate of AO7 removal of ~ 94.974% was obtained at pH 7.599, TNP dose of 0.748 g/L, and AO7 concentration of 28.483 mg/L. The resulting quadratic model is satisfactory with the highest coefficient of determination (R2) between the predicted and experimental data (R2 = 0.98 and adjusted R2 = 0.954). On the other hand, ANNs were successfully employed for modeling of AO7 degradation process. The proposed ANN model was absolutely fitted with experimental results producing the highest R2. Furthermore, root mean square error (RMSE), mean average deviation (MAD), absolute average relative error (AARE), and mean square error (MSE) were examined more to compare the predictive capabilities of ANN and RSM models. The experimental data was well fitted into pseudo-first-order and pseudo-second-order kinetics with more accuracy. Thermodynamic parameters, namely enthalpy, entropy, Gibbs' free energy, and activation energy, were also evaluated to suggest the nature of the degradation process. The increase of temperature was analyzed to be more suitable for the fast removal of AO7 over TNPs. Graphical abstract.

CuO coated olive cake nanocomposites for rapid phenol removal and effective discoloration of high strength olive mill wastewater

The potential of solid olive wastes-based adsorbent (CuO-OC) with photocatalytic power was established for the removal of total phenolics and photocatalytic discolourization of high strength olive mill wastewater (OMW). Clear insight of the FTIR and Brunauer-Emmett-Teller analyses showed that oxygen-containing functional groups of CuO-OC likely participated in the adsorption of total phenols from the OMW via a π-π interaction, hydrogen bonding and electrostatic interaction. Also, the total pore volume of CuO-OC decreased from 0.068 to 0.052 cm³ g^-1 after adsorption, which suggested that phenolics were trapped within the micro- and mesopores of CuO-OC. The adsorption kinetics revealed that ∼82.7-95% of the phenolic compounds were removed within the first 360 min which is relatively faster than adsorbents and methods reported elsewhere. The isotherm results showed that Redlich-Peterson equation fit the experimental data very well with least error (χ² = 1.46-3.19) and high correlation coefficients (0.992-0.996), which suggested a mixture of hetero- and monolayer coverage of the phenolics on the CuO-OC surface. Results obtained herein are of practical interest and the reuse efficiency of CuO-OC remained ∼60% after 5 successive recycling.

Degradation of tetracycline in a schorl/H2O2 system: Proposed mechanism and intermediates

Schorl could perform as an extremely promising catalyst for decomposing tetracycline hydrochloride (TC) due to its high degradation efficiency, low cost, chemical stability, easy recovery and repeatable utilization. Comparisons of TC degradation indifferent systems showed that schorl/H₂O₂ system exhibited the optimum pollutant elimination and TOC removal efficiencies. Kinetics and possible mechanisms of TC degradation were clarified. The OH generated on the schorl surface and O₂^-/HO₂ were the main reactive species responsible for TC oxidation. Six possible intermediates were identified, and possible transform mechanisms and pathways were explored. Active radicals were inclined to attack the CC double bond, dimethylamino and phenolic moieties of TC molecular. The principal intermediate products were generated through N-demethylation, oxidation and rearrangement.