Evaluation of traditional and machine learning approaches for modeling volatile fatty acid concentrations in anaerobic digestion of sludge: potential and challenges.

This study evaluates models for predicting volatile fatty acid (VFA) concentrations in sludge processing, ranging from classical statistical methods (Gaussian and Surge) to diverse machine learning algorithms (MLAs) such as Decision Tree, XGBoost, CatBoost, LightGBM, Multiple linear regression (MLR), Support vector regression (SVR), AdaBoost, and GradientBoosting. Anaerobic bio-methane potential tests were carried out using domestic wastewater treatment primary and secondary sludge. The tests were monitored over 40 days for variations in pH and VFA concentrations under different experimental conditions. The data observed was compared to predictions from the Gaussian and Surge models, and the MLAs. Based on correlation analysis using basic statistics and regression, the Gaussian model appears to be a consistent performer, with high R2 values and low RMSE, favoring precision in forecasting VFA concentrations. The Surge model, on the other hand, albeit having a high R2, has high prediction errors, especially in dynamic VFA concentration settings. Among the MLAs, Decision Tree and XGBoost excel at predicting complicated patterns, albeit with overfitting issues. This study provides insights underlining the need for context-specific considerations when selecting models for accurate VFA forecasts. Real-time data monitoring and collaborative data sharing are required to improve the reliability of VFA prediction models in AD processes, opening the way for breakthroughs in environmental sustainability and bioprocessing applications.

The influence of various chemical oxygen demands on microbial fuel cells performance using leachate as a substrate.

Microbial fuel cells (MFCs), hailed as a promising technology, hold the potential to combat various wastewater pollutants while simultaneously converting their chemical energy into electricity through biocatalysts. This study explores the applicability of a dual compartment MFC (DC-MFC) under varying conditions, targeting the removal of chemical oxygen demand (COD) from landfill leachate and electricity generation. In this setup, anaerobic sludge from a wastewater treatment plant serves as the inoculum in the anode compartment of the MFC, with a Nafion117 membrane acting as the separator between MFC units. The cathode compartments are filled with distilled water and continually aerated for 24 h to enhance air supply. The study assesses the MFC's performance across different COD concentrations, focusing on COD removal, power generation, and Coulombic efficiency. The findings reveal that COD removal efficiency is notably enhanced at higher concentrations of organic matter. Specifically, at a COD concentration of 3325.0 mg L-1, the MFC exhibited the highest COD removal efficiency (89%) and maximum power density (339.41 mWm-2), accompanied by a Coulombic efficiency of 25.5%. However, as the initial substrate concentration increased to 3825 mg L-1, the efficiency decreased to 72%, with a Coulombic efficiency of 13.56% and a power density of 262.34 mWm-2. Optical density levels increased due to bacterial growth at ambient temperature and neutral pH, reflecting the dynamic microbial response within the system.

The effect of ammonia concentration on the treatment of bio electrochemical leachate using MFCs technology.

Microbial fuel cells (MFCs) have garnered attention in bio-electrochemical leachate treatment systems. The most common forms of inorganic ammonia nitrogen are ammonium ([Formula: see text]) and free ammonia. Anaerobic digestion can be inhibited in both direct (changes in environmental conditions, such as fluctuations in temperature or pH, can indirectly hinder microbial activity and the efficiency of the digestion process) and indirect (inadequate nutrient levels, or other conditions that indirectly compromise the microbial community's ability to carry out anaerobic digestion effectively) ways by both kinds. The performance of a double-chamber MFC system-composed of an anodic chamber, a cathode chamber with fixed biofilm carriers (carbon felt material), and a Nafion 117 exchange membrane is examined in this work to determine the impact of ammonium nitrogen ([Formula: see text]) inhibition. MFCs may hold up to 100 mL of fluid. Therefore, the bacteria involved were analysed using 16S rRNA. At room temperature, with a concentration of 800 mg L-1 of ammonium nitrogen and 13,225 mg L-1 of chemical oxygen demand (COD), the study produced a considerable power density of 234 mWm-3. It was found that [Formula: see text] concentrations above 800 mg L-1 have an inhibitory influence on power output and treatment effectiveness. Multiple routes removed the most nitrogen ([Formula: see text]-N: 87.11 ± 0.7%, NO2 -N: 93.17 ± 0.2% and TN: 75.24 ± 0.3%). Results from sequencing indicate that the anode is home to a rich microbial community, with anammox (6%), denitrifying (6.4%), and electrogenic bacteria (18.2%) making up the bulk of the population. Microbial fuel cells can efficiently and cost-effectively execute anammox, a green nitrogen removal process, in landfill leachate.

Utilization of microbial fuel cells as a dual approach for landfill leachate treatment and power production: a review.

Landfill leachate, which is a complicated organic sewage water, presents substantial dangers to human health and the environment if not properly handled. Electrochemical technology has arisen as a promising strategy for effectively mitigating contaminants in landfill leachate. In this comprehensive review, we explore various theoretical and practical aspects of methods for treating landfill leachate. This exploration includes examining their performance, mechanisms, applications, associated challenges, existing issues, and potential strategies for enhancement, particularly in terms of cost-effectiveness. In addition, this critique provides a comparative investigation between these treatment approaches and the utilization of diverse kinds of microbial fuel cells (MFCs) in terms of their effectiveness in treating landfill leachate and generating power. The examination of these technologies also extends to their use in diverse global contexts, providing insights into operational parameters and regional variations. This extensive assessment serves the primary goal of assisting researchers in understanding the optimal methods for treating landfill leachate and comparing them to different types of MFCs. It offers a valuable resource for the large-scale design and implementation of processes that ensure both the safe treatment of landfill leachate and the generation of electricity. The review not only provides an overview of the current state of landfill leachate treatment but also identifies key challenges and sets the stage for future research directions, ultimately contributing to more sustainable and effective solutions in the management of this critical environmental issue.

Smart waste bin monitoring using IoT for sustainable biomedical waste management.

Suboptimal management of healthcare waste poses a significant concern that can be effectively tackled by implementing Internet of Things (IoT) solutions to enhance trash monitoring and disposal processes. The potential utilisation of the Internet of Things (IoT) in addressing the requirements associated with biomedical waste management within the Kaduna area was examined. The study included a selection of ten hospitals, chosen based on the criterion of having access to wireless Internet connectivity. The issue of biomedical waste is significant within the healthcare sector since it accounts for a considerable amount of overall waste generation, with estimates ranging from 43.62 to 52.47% across various facilities. Utilisation of (IoT) sensors resulted in the activation of alarms and messages to facilitate the prompt collection of waste. Data collected from these sensors was subjected to analysis to discover patterns and enhance the overall efficiency of waste management practices. The study revealed a positive correlation between the quantity of hospital beds and the daily garbage generated. Notably, hospitals with a higher number of beds were observed to generate a much greater amount of waste per bed. Hazardous waste generated varies by hospital, with one hospital leading in sharps waste (10.98 kgd-1) and chemical waste (21.06 kgd-1). Other hospitals generate considerable amounts of radioactive waste (0.60 kgd-1 and 0.50 kgd-1), pharmaceuticals, and genotoxic waste (16.19 kgd-1), indicating the need for specialised waste management approaches. The study sheds light on the significance of IoT in efficient waste collection and the need for tailored management of hazardous waste.

Optimizing total ammonia-nitrogen concentration for enhanced microbial fuel cell performance in landfill leachate treatment: a bibliometric analysis and future directions.

Untreated landfill leachate can harm the environment and human health due to its organic debris, heavy metals, and nitrogen molecules like ammonia. Microbial fuel cells (MFCs) have emerged as a promising technology for treating landfill leachate and generating energy. However, high concentrations of total ammonia-nitrogen (TAN), which includes both ammonia and the ammonium ion, can impede MFC performance. Therefore, maintaining an adequate TAN concentration is crucial, as both excess and insufficient levels can reduce power generation. To evaluate the worldwide research on MFCs using landfill leachate as a substrate, bibliometric analysis was conducted to assess publication output, author-country co-authorship, and author keyword co-occurrence. Scopus and Web of Science retrieved 98 journal articles on this topic during 2011-2022; 18 were specifically evaluated and analysed for MFC ammonia inhibition. The results showed that research on MFC using landfill leachate as a substrate began in 2011, and the number of related papers has consistently increased every 2 years, totaling 4060 references. China, India, and the USA accounted for approximately 60% of all global publications, while the remaining 40% was contributed by 70 other countries/territories. Chongqing University emerged as one of the top contributors among this subject's ten most productive universities. Most studies found that maintaining TAN concentrations in the 400-800 mg L-1 in MFC operation produced good power density, pollution elimination, and microbial acclimatization. However, the database has few articles on MFC and landfill leachate; MFC ammonia inhibition remains the main factor impacting system performance. This bibliographic analysis provides excellent references and future research directions, highlighting the current limitations of MFC research in this area.

Bayesian estimation of the parameter of Maxwell-Mukherjee Islam distribution using assumptions of the Extended Jeffrey's, Inverse-Rayleigh and Inverse-Nakagami priors under the three loss functions.

A three-parameter Maxwell-Mukherjee Islam distribution was proposed by applying Maxwell generalized family of distributions introduced by Ishaq and Abiodun [17]. The probability density and cumulative distribution functions of the proposed distribution were defined. The validity test was derived from its cumulative distribution function. The study aimed to obtain a Bayesian estimation of the scale parameter of Maxwell-Mukherjee Islam distribution by using assumptions of the Extended Jeffrey's (Uniform, Jeffrey's and Hartigan's), Inverse-Rayleigh and Inverse-Nakagami priors under the loss functions, namely, Squared Error Loss Function (SELF), Precautionary Loss Function (PLF) and Quadratic Loss Function (QLF), and their performances were compared. The posterior distribution under each prior and its corresponding loss functions was derived. The performance of the Bayesian estimation was illustrated from the basis of quantile function by using a simulation study and application to real life data set. For different sample sizes and parameter values, the QLF and SELF under Jeffrey's and Hartigan's priors produced the same estimates, bias and Mean Squared Error (MSE) just as we observed in their mathematical derivatives. Similarly, the SELF, PLF and QLF under Inverse-Rayleigh and Inverse-Nakagami priors provided the same performance when some parameter values are equal. For some parameter values, the QLF under Inverse-Nakagami and Inverse-Rayleigh priors produced the least values of MSE. In the application to real life data set, the QLF and SELF under Jeffrey's and Hartigan's priors; the SELF, PLF and QLF under Inverse-Rayleigh and Inverse-Nakagami priors provided similar results as observed in the simulation study. Therefore, the study concluded that the QLF under Inverse-Rayleigh and Inverse-Nakagami priors could effectively be used in the estimation of scale parameter of Maxwell-Mukherjee Islam distribution using Bayesian approach.