Electrochemical oxidation of palm oil mill effluent using platinum as anode: Optimization using response surface methodology

Comparative analysis and application of soft sensor models in domestic wastewater treatment for advancing sustainability

This study focuses on the development and evaluation of soft sensor models for predicting NH3-N values in a wastewater treatment process. The study compares the performance of linear regression (LR), neural networks (NN) and random forest regression (RFR) models. The proposed methodology involves optimizing the sequencing batch reactor process using artificial intelligence and an automatic control system. Real-time NH3-N values are obtained by inputting data from electronic conductivity and temperature sensors into the prediction models. Once the predicted NH3-N value falls below the effluent standard, the cycle ends, improving energy efficiency and sustainability by cutting down the agitator and aerator. The research results demonstrate that the RNN-based NH3-N soft sensor built in this study exhibits the best performance, which is promising for wastewater treatment process optimization and evaluation. The results show that sensor model NNR[0.5Y]H exhibits exceptional performance, utilizing recurrent neural network with 5-step input delays. Sensor NNR[0.5Y]H exhibits an R2 of 0.921, an RMSE of 6.110, and an MAE of 4.558. Based on the findings, recurrent neural network (RNN) variants emerge as the most effective modeling technique due to their ability to capture temporal dependencies and handle variable-length sequences. This study provides satisfied performance results for the NNR[0.5Y]H soft sensor model in NH3-N monitoring and process optimization in wastewater treatment, highlighting the effectiveness of recurrent neural networks and their contribution to improving interpretability, accuracy, and adaptability of soft sensor models.

Chemical oxygen demand and tannin/lignin removal from paper mill wastewater by electrocoagulation combined with peroxide and hypochlorite treatments

The present investigation sought to assess the practicality of utilizing a combined pre-treatment approach comprising electrocoagulation, peroxide, and hypochlorite treatments for the removal of chemical oxygen demand (COD) and tannin/lignin from paper mill wastewater. The study aimed to optimize the operating parameters with a view to maximizing the removal efficiencies while minimizing energy consumption. A pair of iron electrodes were used as anode and cathode in the study, and the main operating parameters were determined as initial pH, applied current, treatment time and oxidant dosage/COD ratio. Response surface methodology (RSM) was used to evaluate the effect of these parameters on COD and tannin/lignin removals. The primary findings of the investigation indicated that the integration of electrocoagulation with peroxide and hypochlorite treatments exhibited efficacy in removing COD, tannin/lignin, colour, phenol, and turbidity from paper mill wastewater. The optimized conditions resulted in COD removal efficiencies of 48.13 ± 2.2% and 29.53 ± 1.4% for EC with H2O2 and Ca(OCl)2, respectively. Tannin/lignin removal efficiencies were 92.59 ± 3.6% and 94.09 ± 1.8% for EC-H2O2 and EC-Ca(OCl)2, respectively. The specific energy consumption (SEC) values showed that EC-Ca(OCl)2 required 7 times more energy than EC-H2O2 for removing 1 kg COD. The principal deduction drawn from the study was that EC-H2O2 pre-treatment demonstrated superior COD removal efficiency and lower energy consumption, while EC-Ca(OCl)2 pre-treatment exhibited greater efficiency in removing toxic and recalcitrant pollutants. In future studies, it would be useful to conduct research to increase COD removal efficiency in addition to tannin/lignin removal in EC-Ca(OCl)2 process.

Demonstration of π-π Stacking at Interfaces: Synthesis of an Indole-Modified Monodisperse Silica Microsphere SiO2@IN

In the present study, a novel silane coupling agent, designated INSi, was synthesized via a facile synthetic route, incorporating indole-functional moieties. This agent was further employed for the surface modification of homemade silica nanomicrospheres (SMPs). The ensuing nanomicrosphere composite, denoted as SiO2@IN, exemplified pronounced interfacial π-π interactions. Optimization of the reaction conditions was conducted using the response surface optimization technique. Subsequent validation of interfacial π-π interactions was accomplished through a synergistic approach, integrating theoretical calculations and comprehensive analyses of spectral and morphological attributes exhibited by the SiO2@IN.

Rapid Surface Modification of Stainless Steel 304L Electrodes for Microbial Electrochemical Sensor Application

Electrogenic microorganisms serve as important biocatalysts for microbial electrochemical sensors (MESes). The electrical signal produced is based on the rate of electron transfer between the microbes and electrodes, which represents the biotoxicity of water. However, existing MESes require complex and sophisticated fabrication methods. Here, several low-cost and rapid surface modification strategies (carbon powder-coated, flame-oxidized, and acid-bleached) have been demonstrated and studied for biosensing purposes. Surface-modified MESe bioanodes were successfully applied to detect multiple model pollutants including sodium acetate, ethanol, thinner, and palm oil mill effluent under three different testing sequences, namely, pollutant incremental, pollutant dumping, and water dilution tests. The carbon powder-coated bioanode showed the most responsive signal profile for all the three tests, which is in line with the average roughness values (Ra) when tested with atomic force microscopy. The carbon powder-coated electrode possessed a Ra value of 0.844, while flame-oxidized, acid-bleached, and control samples recorded 0.323, 0.336, and 0.264, respectively. The higher roughness was caused by the carbon coating and provided adhesive sites for microbial attachment and growth. The accuracy of MESe was also verified by correlating with chemical oxygen demand (COD) results. Similar to the sensitivity test, the carbon powder-coated bioanode obtained the highest R2 value of 0.9754 when correlated with COD results, indicating a high potential of replacing conventional water quality analysis methods. The reported work is of great significance to showcase facile surface modification techniques for MESes, which are cost-effective and sustainable while retaining the biocompatibility toward the microbial community with carbon-based coatings.

Regulation of ash slagging behavior of palm oil decanter cake by alum sludge addition

Combustion of palm oil decanter cake (PODC) is a propitious alternative waste to energy means. However, the mono-combustion of PODC prompt severe ash slagging behavior which give rise to reduction in heat transfer and also shorten the lifespan of combustion reactors. In this study, alum sludge (AS) was introduced at different proportion of 30%, 50% and 70% to revamp the slagging characteristics of PODC during combustion. The addition of AS improved ash fusion temperature of PODC during co-combustion as ash fusion temperature increased significantly under high AS dosage. Slagging and fouling indices showed that at 50% AS addition, slagging tendency of the co-combustion ashes can be ignored. The predictive model for PODC-AS combustion showed good correlation coefficient with 0.89. Overall, co-combustion of PODC and AS is an ideal ash related problem-solving route. The proposed PODC slagging preventive method by AS was based on: (1) limited amount of aluminum content in PODC-AS system resulted in development of refractory ash (2) reduction in proportion of basic oxide which act as ash bonding glue played important role in the regulation of slagging (3) reduction of cohesive bond by formation of spongy and porous structure which prevented ash slagging.

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).

Efficient Electrochemical Oxidation of Chloramphenicol by Novel Reduced TiO2 Nanotube Array Anodes: Kinetics, Reaction Parameters, Degradation Pathway and Biotoxicity Forecast

The key component of electrochemical advanced oxidation technology are high-efficiency anodes, and highly efficient and simple-to-prepare materials have generated a lot of interest. In this study, novel self-supported Ti³⁺-doped titanium dioxide nanotube arrays (R-TNTs) anodes were successfully prepared by a two-step anodic oxidation and straightforward electrochemical reduction technique. The electrochemical reduction self-doping treatment produced more Ti³⁺ sites with stronger absorption in the UV-vis region, a band gap reduction from 2.86 to 2.48 ev, and a significant increase in electron transport rate. The electrochemical degradation effect of R-TNTs electrode on chloramphenicol (CAP) simulated wastewater was investigated. At pH = 5, current density of 8 mA cm^-2, electrolyte concentration of 0.1 M sodium sulfate (Na₂SO₄), initial CAP concentration of 10 mg L^-1, CAP degradation efficiency exceeded 95% after 40 min. In addition, molecular probe experiments and electron paramagnetic resonance (EPR) tests revealed that the active species were mainly •OH and SO₄^-, among which •OH played a major role. The CAP degradation intermediates were discovered using high-performance liquid chromatography-mass spectrometry (HPLC-MS), and three possible degradation mechanisms were postulated. In cycling experiments, the R-TNTs anode demonstrated good stability. The R-TNTs prepared in this paper were an anode electrocatalytic material with high catalytic activity and stability, which could provide a new approach for the preparation of electrochemical anode materials for difficult-to-degrade organic compounds.

Self-circulation of oily spent hydrodesulphurization (HDS) catalyst by catalytic pyrolysis for high quality oil recovery

In this study, roasted spent HDS ash (sHDSc-A) was used for the first time to catalytically pyrolyze oily spent HDS catalysts (sHDSc) to improve the yield and quality of pyrolysis oil. The results showed that sHDSc-A promoted the decomposition of coke in oily sHDSc, resulting in the recovery of more oil and gas. Meanwhile, sHDSc-A significantly improved the quality of the pyrolysis oil. They inhibited the aromatization of alkanes to increase the saturation of the pyrolysis oil from 59.39% to 74.25% and the H/C radio from 1.62 to 1.72; promoted the decomposition of long-chain alkanes to increase the content of C11-C22 from 41.97% to 61.99%; enhanced the conversion of carboxylic acids to ketones led to the reduction of heteroatomic compounds such as N (56.10%-45.39%), S (66.95%-56.59%), and O (45.26%-26.70%) in the pyrolysis oil. The promotion of sHDSc-A in the pyrolysis process is attributed to the catalytic effect of the metal oxides in sHDSc-A. Among them, Al₂O₃ and Fe₂O₃ can promote decarboxylation of carboxylic acids and reduce O mobility, while MoO₃ and Fe₂O₃ play a significant role in reducing coke and increasing pyrolysis oil. NiO can also promote methane vapor reforming, and thus increase the production of H₂ in non-condensable gas. This study achieves self-circulation of oily sHDSc with a "waste-treatment-waste" strategy that presents the advantage of value-added energy recovery and waste reuse.

Application of CNN and ANN in assessment the effect of chemical components of biological nanomaterials in treatment of infection of inner ear and environmental sustainability

Nanoparticles (NPs) are a promising alternative to antibiotics for targeting microorganisms, especially in the case of difficult-to-treat bacterial illnesses. Antibacterial coatings for medical equipment, materials for infection prevention and healing, bacterial detection systems for medical diagnostics, and antibacterial immunizations are potential applications of nanotechnology. Infections in the ear, which can result in hearing loss, are extremely difficult to cure. The use of nanoparticles to enhance the efficacy of antimicrobial medicines is a potential option. Various types of inorganic, lipid-based, and polymeric nanoparticles have been produced and shown beneficial for the controlled administration of medication. This article focuses on the use of polymeric nanoparticles to treat frequent bacterial diseases in the human body. Using machine learning models such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), this 28-day study evaluates the efficacy of nanoparticle therapy. An innovative application of advanced CNNs, such as Dense Net, for the automatic detection of middle ear infections is reported. Three thousand oto-endoscopic images (OEIs) were categorized as normal, chronic otitis media (COM), and otitis media with effusion (OME). Comparing middle ear effusions to OEIs, CNN models achieved a classification accuracy of 95%, indicating great promise for the automated identification of middle ear infections. The hybrid CNN-ANN model attained an overall accuracy of more than 0.90 percent, with a sensitivity of 95 percent and a specificity of 100 percent in distinguishing earwax from illness, and provided nearly perfect measures of 0.99 percent. Nanoparticles are a promising treatment for difficult-to-treat bacterial diseases, such as ear infections. The application of machine learning models, such as ANNs and CNNs, can improve the efficacy of nanoparticle therapy, especially for the automated detection of middle ear infections. Polymeric nanoparticles, in particular, have shown efficacy in treating common bacterial infections in children, indicating great promise for future treatments.

Application of logistic regression, support vector machine and random forest on the effects of titanium dioxide nanoparticles using macroalgae in treatment of certain risk factors associated with kidney injuries

The use of titanium dioxide (TiO₂) nanoparticles in many biological and technical domains is on the rise. There hasn't been much research on the toxicity of titanium dioxide nanoparticles in biological systems, despite their ubiquitous usage. In the current investigation, samples were exposed to various dosages of TiO₂ nanoparticles for 4 days, 1 month, and 2 months following treatment. ICP-AES was used to dose TiO₂ into the tissues, and the results showed that the kidney had a significant TiO₂ buildup. On the other hand, apoptosis of renal tubular cells is one of the most frequent cellular processes contributing to kidney disease (KD). Nevertheless, the impact of macroalgal seaweed extract on KD remains undetermined. In this work, machine learning (ML) approaches have been applied to develop prediction algorithms for acute kidney injury (AKI) by use of titanium dioxide and macroalgae in hospitalized patients. Fifty patients with (AKI) and 50 patients (non-AKI group) have been admitted and considered. Regarding demographic data, and laboratory test data as input parameters, support vector machine (SVM), and random forest (RF) are utilized to build models of AKI prediction and compared to the predictive performance of logistic regression (LR). Due to its strong antioxidant and anti-inflammatory powers, the current research ruled out the potential of using G. oblongata red macro algae as a source for a variety of products for medicinal uses. Despite a high and fast processing of algorithms, logistic regression showed lower overfitting in comparison to SVM, and Random Forest. The dataset is subjected to algorithms, and the categorization of potential risk variables yields the best results. AKI samples showed significant organ defects than non-AKI ones. Multivariate LR indicated that lymphocyte, and myoglobin (MB) ≥ 1000 ng/ml were independent risk parameters for AKI samples. Also, GCS score (95% CI 1.4-8.3 P = 0.014) were the risk parameters for 60-day mortality in samples with AKI. Also, 90-day mortality in AKI patients was significantly high (P < 0.0001). In compared to the control group, there were no appreciable changes in the kidney/body weight ratio or body weight increases. Total thiol levels in kidney homogenate significantly decreased, and histopathological analysis confirmed these biochemical alterations. According to the results, oral TiO₂ NP treatment may cause kidney damage in experimental samples.

Using fish scales as a new biosorbent for adsorption of nickel and copper ions from wastewater and investigating the effects of electric and magnetic fields on the adsorption process

In this study, fish scales (Pomadasys kaakan's scales) were used as new biosorbent for removing Ni²⁺ and Cu²⁺ ions from wastewater. The effects of electric and magnetic fields on the absorption efficiency were also investigated. The effects of sorbent content, ion concentration, contact time, pH, electric field (EF), and magnetic field (MF) on absorption efficiency were assertained. In addition, the isotherm of absorption was studied in this work. This study revealed that electric field and magnetic field have significant effects on the absorption efficiency of ions from wastewater. An increase in the electric field enhanced the removal percentage of the ions and accelerated the absorption process by up to 40% in comparison with the same condition without an electric field or a magnetic field. By increasing contact time from 10 to 120 min, the removal of Ni²⁺ ions was increased from 1% to 40% and for Cu²⁺ ions, the removal increased from 20% to almost 95%, respectively. In addition, increasing pH, ion concentration and scales dose increased removal percentage effectively. The results indicated that using fish scales for Cu²⁺ ions absorption is ideal due to the very high removal percentage (approximately 95%) without using either an electric or magnetic field.

Evaluation of waste management and energy saving for sustainable green building through analytic hierarchy process and artificial neural network model

A significant portion of the solid waste filling landfills worldwide is debris from construction and demolition projects. Across the world, a significant portion of the solid waste filling landfills is made up of construction and demolition waste. Recycling construction waste may help cut down on the quantity of waste sent to landfills and the requirement for energy and other natural resources. To help with construction waste reduction, a management hierarchy that begins with rethink, reduce, redesign, refurbish, reuse, incineration, composting, recycle, and eventually disposal is likely to be effective. The objective of this research is to investigate the viability of the Analytic Hierarchy Process (AHP) as a data gathering instrument for the development of a solid waste management assessment tool, followed by an examination of an artificial neural network (ANN). Using a standardized questionnaire, all data was gathered from waste management practitioners in three industry sectors. The survey data was subsequently analyzed using ANN and later AHP. The suggested framework consisted of four components: (1) the development of different level structures for fluffy AHP, (2) the calculation of weights, (3) the collection of data, and (4) the making of decisions. An ANN feedforward with error back propagation (EBP) learning computation is coupled to identify the association between the items and the store execution. It was found that the combination of AHP and ANN has emerged as a key decision support tool for landfilling, incineration, and composting waste management strategies, taking into account the environmental profile and economic and social characteristics of each choice. Composting has the highest sustainable performance when a balanced weight distribution of criteria is assumed, especially if the environmental component is considered in comparison to the other criteria. However, if social and economic features are addressed, incineration or landfilling have more favorable characteristics, respectively.

Application of nano remediation of mine polluted in acid mine drainage water using machine learning model

Acid mine drainage (AMD) is the term used to describe drainage from coal mines with high sulfur-bearing rocks. The oxidative weathering of metal sulfides leads to AMD. The acidic environment corrodes more harmful compounds in the soil, which is spread throughout the working area. One such significant metal is copper, which is extracted in massive quantities from ores rich in sulfide. A copper-extraction resin might be created by combining diatomaceous earth (DE) particles with polyethyleneimine (PEI), which is shown to have great selectivity and affinity for copper. In this effort, PEI-DE particles' copper absorption level was examined by using synthetic and actual acid mine drainage samples at varied pH values. The findings of the copper uptake particles have been examined through the Support Vector Machine (SVM) model. Using the n-fold 14 cross-validation approach, the quantities of parameters and C are estimated to be 0.001 and 0.01, respectively. The SVM analysis was correct, and the findings indicated that copper could bind to the material efficiently and preferentially at pH 4. Subsequent water elution studies at a pH value of 1 confirmed the pH-reliant interaction between dissolved Cu and PEI by demonstrating full release of the adsorbed Cu. In this research, the copper absorption of PEI-DE particles from synthetic and genuine AMD specimens was studied based on several pH conditions. The findings suggest that copper may attach to the material effectively and preferentially at pH 4. Studies of filtering water at pH1 later confirmed that all of the adsorbed Cu was released. This shows that the interaction between PEI and dissolved Cu depends on PH.