Case Studies in Chemical and Environmental Engineering最新文献

Monitoring river water quality is crucial for safeguarding public health, protecting ecosystems, and ensuring economic sustainability. It helps detect contaminants, ensures drinking water safety, and facilitates early intervention for environmental protection and legal compliance. The objective of this study is to evaluate multiple machine learning algorithms to analyze water quality parameters in computing water quality index (WQI) and classification thereof, aiming to devise a reliable method for forecasting water quality with high accuracy. In this study, fourteen machine learning classifiers applied include Support Vector Machine (SVM), Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multilayer Perceptron (MLP), K-Nearest Neighbor (KNN), Naïve Bayes, Gradient boosting, AdaBoost, Bagging, Extra Trees, Quadratic Discriminant Analysis (QDA), XGBoost, and CATBoost. A total of 1096 sample data was used where each data consists of nineteen analytical water quality parameters. To assess the performance of various classifiers, several evaluation techniques were utilized including confusion matrices, classification reports detailing precision and accuracy ratios, and Receiver Operating Characteristic (ROC) curves. The study also utilizes explainable AI (LIME and SHAP) to provide clear insights into the decision-making processes used to classify river water quality. The results indicated that all ML models demonstrate satisfactory performance in predicting WQI. Among the classifiers used, Gradient Boosting achieves the highest Accuracy (99.64 %), Precision (0.95), Recall (0.96), and F1-Score (0.95), indicating its superior ability to correctly classify instances and suggesting a balanced performance across different evaluation metrics. The analysis presented in this article holds the promise of providing accurate water quality data to researchers, thereby enhancing monitoring effectiveness through the application of machine learning techniques.

Vanillin (4-hydroxy-3-methoxybenzaldehyde) is an important platform chemical compound that is invariably produced from pyrolysis of various categories of biomass. Hydrodeoxygenation (HDO) of vanillin has been a thematic topic in catalysis with the underlying aim to devise processes and reactions for catalytic upgrading of bio-oil and in the production of value-added products. Herein, we investigate the HDO of an evaporated stream of vanillin over a 4 % load of palladium supported on ceria; a Pd/CeO₂ catalyst. The synthesized catalyst was characterized by various techniques including XRD, XPS, EDX-SEM, HR-TEM, and TPR. The HDO reaction (carried out at temperatures from 100 °C to 300 °C at 10 °C/min ramp rate with a 5 % H₂ feed ratio) resulted in a 95 % conversion of vanillin with an 85 % yield of guaiacol (2-methoxyphenol). Minute loads of alkylbenzene compounds (mainly xylene and ethylbenzene) also emerged. DFT computations elucidate pathways for the observed formation of guaiacol where synergistic effects of both Pd and vacant oxygen sites are highlighted. Overall, we presented a viable HDO route for an oxygenated biomass model compound at mild operational conditions (intermediate temperatures, ambient pressure, and moderate H₂/Feed ratio).

The global problem of water scarcity continues to grow because of population growth and climate change. Membrane distillation (MD) stands out as a progressing technology, demonstrating the potential for integration with operating systems powered by solar energy. Despite the increasing interest in solar-powered MD, existing literature primarily focuses on the technical aspects of the technology. There is a notable lack of comprehensive analyses that explore broader research trends, key contributors, and thematic evolution within this field. This paper addresses this gap by employing bibliometric analysis and machine learning approaches to explore trends in solar membrane desalination based on publications from 2014 to 2023. 991 publications were analyzed from various aspects, such as publications, countries, institutions, and research trends. Hot keywords include “membrane distillation”, “reverse osmosis”, “energy efficiency”, and “evaporation”. China had the highest number of publications and h-index, followed by the United States, and Saudi Arabia. The study clustered the keywords into three themes: membrane distillation, membrane materials, and reverse osmosis. Analysis of the thematic map reveals seven main research topics in solar-powered membrane desalination, classified according to their degree of relevance. Finally, future directions of membrane desalination are highlighted, including exploring economic and environmental aspects.

Response Surface Methodology and Box-Behnken Design have been applied to optimize microwave-assisted roasting of refractory gold ore. The roasting is used as a pretreatment for refractory gold ore to increase gold recovery during leaching. The roasting step consumes high energy and produces high sulfur emissions into the atmosphere. Optimization aims to obtain optimum roasting conditions with minimum energy consumption and sulfur emissions. The effects of microwave power (100–400W), NaClO₃ composition (120–360 kg/tonne), water quantity (0–120 kg/tonne), and duration of roasting (5–30 minutes) have been investigated at the preliminary stage. In the optimization stage, three variables were studied with the roasting time fixed at 5 minutes. The optimum conditions for microwave-assisted roasting of refractory gold ore were achieved at 200W, NaClO₃ of 200 kg/tonne ore, and water of 150 kg/tonne ore. Based on the optimization model, the predicted temperature and sulfur oxidation are 394 °C and 67.16 %, respectively. Model validation showed that the actual roasting temperature and sulfur oxidation are 404 °C and 67.28 %, respectively. The differences between the predicted and actual values of temperature and sulfur oxidation are 2.5 % and 0.18 %, respectively. With an accuracy surpassing 95 %, the optimization model is capable of predicting both temperature and sulfur oxidation.

[Cu2(EDA)2] [Cu (CN)2] 2.H2O] (MOF1) and [H2DB] [Cu4(CN)6] 2.H2O (MOF2) were synthesized and studied as a corrosion inhibitor for aluminium (Al) in 1.0 M HCl solution. The synthesis was done at room temperature via chemical method. The crystals of MOF1 and MOF2 were obtained after filtration and coating with cold H2O. The MOF precipitate was characterized by X-ray diffraction (XRD) techniques. The study aimed to evaluate the corrosion inhibition efficacy of the MOFs using mass loss, potentiodynamic polarization, and EIS electrochemical impedance techniques. Both MOF1 and MOF2 were physically adsorbed on the surface of the aluminium and acted as mixed-type inhibitors affecting metal dissolution and hydrogen evolution reactions. The inhibitors conformed to the Henry adsorption isotherm model, indicating successful adsorption on the aluminium surface. The mass loss analysis (MLA) results were obtained at (298, 308, and 318) K, and for potentiodynamic polarization (PP) and electrochemical impedance spectroscopy (EIS) results were obtained at 298 K. Increasing inhibitor doses led to increased inhibition efficiency (%IE), corresponding to 93 % for MOF2 and 91 % for MOF1 at 2.5x10⁻⁴M. The adsorption of inhibitors on Al surfaces has been calculated and discussed by a Henry isotherm. The inhibitors that were created showed great effectiveness, with a noticeable increase in their inhibitory efficiency (%IE) as the dosage was raised and the temperature was lowered. The synthesized inhibitors acted as mixed-type inhibitors based on polarization curves. The surface of Al was coated with a thin film of inhibitors, confirming the protective effect. A scanning electron microscope (SEM) technique was used to study the surface morphology of a sample of aluminium. A cell construction model and electron density map were used as theoretical calculations. The results from mass reduction, potentiodynamic polarization, and EIS electrochemical impedance techniques showed good agreement, validating the effectiveness of the MOFs as corrosion inhibitors.

This study aimed to compare the water age in a drinking water distribution network (DWDN) using tracers and EPANET. The results indicate that all DWDN have residence times within the “short” time. established by the EPA and does not represent quality problems. These two techniques provided similar estimates of water age with small differences at points close to the treatment plant. This difference may be due to the fact that tracers can be retained in pipes, which overestimates the age of the water; meanwhile, EPANET could underestimate residence times due to the calibration or simplified representation of the network.

The significant impact of microplastics in the marine environment has sparked global concern. These tiny plastic particles travel from land to the estuary through rivers, where they become intricately distributed within the estuarine dynamics. The spatial distribution of plastic debris and sedimentation in the estuary is mainly influenced by the dynamics of the estuary, posing a scientific challenge that demands immediate attention. The main objective of this study is to analyse the microplastic contamination in the water samples collected from the Azhikkal estuary in Kannur, India, subsequent to the establishment of a seaport in the region, using a home assembled micro-Raman spectrometer. This research sheds light on the extensive prevalence of microplastics detected in the vicinity of the estuary’s entrance, with a specific focus on the consequences of seaport construction in the surrounding region. Within the surveyed region, a considerable quantity of 1260 microplastic particles and 1480 anthropogenic particles were identified. The predominant plastic varieties observed in this particular area consist of polystyrene (38 %), polysulfone (5 %), polypropylene (1 %), and polyethylene terephthalate (1 %). The predominant microplastics discovered in this region consisted mainly of fragments (82 %) and fibers (15 %), varying in sizes from 10 to 100 μm (36 %), resulting in a higher surface area to volume ratio. The existence of red and blue pigments, such as copper phthalocyanine and indigo blue, in plastic pollution discovered in this vicinity is causing alarm over the potential harmful consequences on marine organisms that rely on these ecosystems. The identification of these pigments in the estuarine region and aquatic environments across the entire nation has not been adequately pursued. Additionally, this research delves into the spread of microplastics in the murky estuarine setting, considering the significant impact of sea surface wind and alterations in buoyancy following the formation of a biofilm on their surface. This leads to the microplastics acquiring hydrophilic characteristics within the turbid estuarine environment.

The primary goal of the current research was predicting the optimum operational parameters for removing of Fe (II) from aqueous solutions by Exhausted Coffee Husk (ECH) biochar, through batch mode experiments using a Box-Behnken Design (BBD) within a Response Surface Methodology (RSM) framework. The effect operational factors for instance, pH, contact time and the dosage of ECH-biochar were investigated in a certain range, prior to conducting the BBD experiments. The pH was in a range of 4–9, while the contact time was in between 30 and 150 minutes and the dosage 0.05–2.5 g. The results showed that the optimum operational parameters for ECH biochar as biosorbent for Fe ions removal were 7, 0.102 g, 54.49 minutes for pH, contact time and dosage, respectively, with the RE and biosorption capacity (q_t) were 99.91 % and 73.09 mg/g. The validation test showed the similar results were obtained for RE and q_t with %RSD were 0.7 %. The process of Fe biosorption corresponds effectively with a modified Langmuir isotherm model, and the prediction of the rate constant is reliably achieved using the pseudo-second-order kinetic model. Furthermore, analyses of scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDX) and X-ray diffraction (XRD) showed ion exchange as the Fe removal mechanism by the ECH-BC, while Fourier-transform infrared spectroscopy (FT-IR) also showed the role of structural alterations on the surface of ECH-BC subsequent to the biosorption of Fe.

Cellulose nanofibrils (CNF) are promising renewable materials due to their high surface area, abundance, and ease of modification. This study explores the impact of source material on CNF properties for diverse applications like drug delivery and composites. CNF were prepared from corn cob (CC), bagasse (BG), waste wood (WW), and bacterial cellulose (BC) using TEMPO-mediated oxidation. Microcrystalline cellulose (MCC) was also oxidized (MCC-ox) for comparison. The transparency, chemical structure, crystallinity index, and surface charge of the resulting CNF were investigated. As a result, all CNF yields ranged from 25 % to 34 %. FT-IR analysis confirmed successful TEMPO oxidation by detecting carboxyl groups on all CNF surfaces. BC-derived CNF displayed the second-highest transparency after MCC. Surface charge analysis revealed the highest carboxyl content in MCC-ox (8828.39 mmol/kg), followed by CNF-BC (8438.84 mmol/kg), CNF-CC (7687.24 mmol/kg), CNF-WW (6720.43 mmol/kg), and CNF-BG (5505.61 mmol/kg). XRD analysis indicated the highest crystallinity index in MCC-ox (83.40 %) due to its high purity, followed by CNF-BC (82.52 %) likely due to its nanostructure and high purity, and CNF-CC (78.14 %) potentially due to the rigid and dense structure of corn cobs. These findings provide valuable insights into selecting CNF with the desired characteristics for various fields such as material science, nanotechnology, and biomedicine.

The main objective of this research is to improve the quality of bio-oil from combusting palm fronds by integrating HDPE plastic waste through a co-pyrolysis process using metal oxides (copper, zinc, iron, and cobalt) based on mordenite (MOR) zeolite. Catalyst characterization revealed alterations in physisorption properties and acidity post-metal oxide addition. Fe₂O₃/MOR exhibits notable gasoline selectivity (36.81 %), while CoO/MOR enhances kerosene (29.05 %) and diesel (29.95 %) fractions. Both catalysts demonstrate superior activity, yielding fuel compounds with high heating values (HHV) (30.91 MJ/kg and 31.25 MJ/kg, respectively). This novel approach holds promise for sustainable bio-oil production with tailored fuel properties.