A review on the lignocellulosic derived biochar-based catalyst in wastewater remediation: Advanced treatment technologies and machine learning tools

Influence of biochar on the partitioning of iron and arsenic from paddy soil contaminated by acid mine drainage

Paddy fields contaminated by arsenic-containing acid mine drainage (AMD) may also have rich iron in soil. Whether this iron can be loaded onto biochar to passivate the dissolved arsenic is worth further exploration. Soil was mixed with biochar prepared at 400, 550, and 700 °C and incubated under alternating anaerobic and aerobic conditions. Soil, soil solution and biochar samples were analysed using ICP-MS, FTIR, SEM, XPS, etc. The results showed that biochar prepared at lower pyrolysis temperatures contained a higher number of functional groups. Under the combined action of microorganisms, primarily from the Firmicutes phylum, biochar promoted the dissolution of arsenic-containing iron oxides in soil, with the residual arsenic also undergoing transformation. The biochar rapidly loaded dissolved iron onto its surface, likely in the form of Fe3O4 and FeOOH, and adsorbed arsenic primarily as As(III). Although the iron oxides detached over time, they were more stable on the biochar prepared at 400 °C compared to those prepared at higher pyrolysis temperatures. Meanwhile, the arsenic content on the biochar increased, raising the As/Fe molar ratio to above that of the soil. This study lays the foundation for further exploring the long-term use of biochar in AMD-contaminated paddy fields.

Application of machine learning for environmentally friendly advancement: exploring biomass-derived materials in wastewater treatment and agricultural sector - a review

There are several uses for biomass-derived materials (BDMs) in the irrigation and farming industries. To solve problems with material, process, and supply chain design, BDM systems have started to use machine learning (ML), a new technique approach. This study examined articles published since 2015 to understand better the current status, future possibilities, and capabilities of ML in supporting environmentally friendly development and BDM applications. Previous ML applications were classified into three categories according to their objectives: material and process design, performance prediction and sustainability evaluation. ML helps optimize BDMs systems, predict material properties and performance, reverse engineering, and solve data difficulties in sustainability evaluations. Ensemble models and cutting-edge Neural Networks operate satisfactorily on these datasets and are easily generalized. Ensemble and neural network models have poor interpretability, and there have not been any studies in sustainability assessment that consider geo-temporal dynamics; thus, building ML methods for BDM systems is currently not practical. Future ML research for BDM systems should follow a workflow. Investigating the potential uses of ML in BDM system optimization, evaluation and sustainable development requires further investigation.

Ammonium capture Kinetic, Capacity, and Prospect of Rice Husk Biochar produced by different pyrolysis conditions

This study explores the potential application of rice husk biochars, categorized by their pH (acidic, pH 5.98; neutral, pH 7.02; and alkali, pH 11.21) and particle sizes (micron-scale and sub-centimeter) in aquatic ecosystems for efficient removal of ammonium (NH4+). To assess the NH4+ adsorption capacity of the rice husk biochars, both NH4+ adsorption kinetics and isotherms were employed. Additionally, we propose future prospects for utilizing rice husk biochar as an efficient adsorbent based on a review of previous studies. Our findings suggest that the NH4+adsorption capacity of rice husk biochars is primarily influenced by their surface characteristics, specifically surface area of rice husk biochars and loss of acidic functional groups. In this study, the neutral rice husk biochars, which had the highest surface area at 9.86 m2 g-1, exhibited the highest NH4+adsorption performance at 1.12 mg g-1 (micron-scale) and 0.94 mg g-1 (sub-centimeter) compared to acidic and alkali rice husk biochars. Additionally, particle size control proves to be a promising strategy for enhancing adsorption efficiency of rice husk biochars, with the micron-scale rice husk biochars being 1.19-fold higher than sub-centimeter ones. However, before implementing biochar-based pollutant removal strategies in aquatic ecosystems, several considerations (e.g., the potential harmfulness of inner components in biochar, side effects of biochar on aquatic life, and tracking the fate of biochar in aquatic ecosystems) must be addressed. By addressing these concerns, we can expect to expand the practical application of biochar for remediation in aquatic environments, contributing to the effective management of pollutants.

Fe3C@Fe decorated carbonized wood Fiber catalyst for organic dyes degradation: Preparation, characterization and mechanism

The extensive use of organic dyes has led to significant water pollution. Using lignocellulosic waste as a precursor for catalysts preparation for sewage remediation presents an effective alternative with numerous advantages in sustainability, cost-effectiveness, and environmental compatibility. In this work, wood fiber waste collected from wood processing plants was decorated with Prussian blue (PB) and then annealed to produce carbonized wood fiber catalyst (Fe3C@Fe-CB). In the presence of peroxymonosulfate (PMS), the prepared catalyst could achieve over 98.69 % efficiency in degrading methylene blue (MB) solutions (20 mg/L) in 60 min across a pH range of 3 to 11. Electrochemical test and electron paramagnetic resonance (EPR) analysis respectively verified that electron transfer pathway and radical pathway were the key factors for the degradation of MB. Cyclic degradation tests demonstrated that the degradation efficiency remained above 93.67 % after five recycling experiments. Moreover, the used magnetic catalyst can be easily recycled by magnet. This study proposed a facile and sustainable carbonized wood fiber catalyst decorated by Fe3C@Fe-CB, which could realize efficient elimination of organic dyes. Moreover, we offered a novel choice for dyes-polluted water treatment and paving a new route for converting low-value lignocellulosic waste to high-value utilizations.

Coconut shell-based biochars produced by an innovative thermochemical process for obtaining improved lignocellulose-based adsorbents

The urgent need for a simple and cost-effective thermochemical process to produce biochar has prompted this study. The aim was to develop a straightforward thermochemical process under O2-limited conditions for the production of coconut-based biochar (CBB) and to assess its ability to remove methylene blue (MB) through adsorption, comparing it with CBB produced by slow pyrolysis. CBBs were obtained under different atmospheric conditions (O2-limited, muffle furnace biochar (MFB); and inert, pyrolytic reactor biochar (PRB)), at 350, 500, and 700 °C, and for 30 and 90'. MFB and PRB were characterized using FTIR, RAMAN, SEM, EDS, and XRD analyses. Adsorption tests were conducted using 1.0 g L-1 of MFB and PRB, 10 mg L-1 of MB at 25 °C for 48 h. Characterization revealed that atmospheric conditions significantly influenced the yield and structural features of the materials. PRB exhibited higher yields and larger cavities than MFB, but quite similar spectral features. Adsorption tests indicated that MFB and PRB had qt values of 33.1 and 9.2 mg g-1, respectively, which were obtained at 700 °C and 90', and 700 °C and 30', respectively. This alternative method produced an innovative and promising lignocellulose-based material with great potential to be used as a biosorbent.

A comprehensive review of the "black gold catalysts" in wastewater treatment: Properties, applications and bibliometric analysis

A significant amount of effort has been devoted to the utilization of biochar-based catalysts in the treatment of wastewater. By virtue of its abundant functional groups and high specific surface area, biochar holds significant promise as a catalyst. This article presents a comprehensive systematic review and bibliometric analysis covering the period from 2009 to 2024, focusing on the restoration of wastewater through biochar catalysis. The production, activation, and functionalization techniques employed for biochar are thoroughly examined. In addition, the application of advanced technologies such as advanced oxidation processes (AOPs), catalytic reduction reactions, and biochemically driven processes based on biochar are discussed, with a focus on elucidating the underlying mechanisms and how surface functionalities influence the catalytic performance of biochar. Furthermore, the potential drawbacks of utilizing biochar are also brought to light. To emphasize the progress being made in this research field and provide valuable insights for future researchers, a scientometric analysis was conducted using CiteSpace and VOSviewer software on 595 articles. Hopefully, this review will enhance understanding of the catalytic performance and mechanisms pertaining to biochar-based catalysts in pollutant treatment while providing a perspective and guidelines for future research and development efforts in this area.

Photocatalytic degradation of drugs and dyes using a maching learning approach

The waste management industry uses an increasing number of mathematical prediction models to accurately forecast the behavior of organic pollutants during catalytic degradation. With the increasing quantity of waste generated, these models are critical for reinforcing the efficiency of wastewater treatment strategies. The application of machine-learning techniques in recent years has notably improved predictive models for waste management, which are essential for mitigating the impact of toxic commercial waste on global water supply. Organic contaminants, dyes, pesticides, surfactants, petroleum by-products, and prescription drugs pose risks to human health. Because traditional techniques face challenges in ensuring water quality, modern strategies are vital. Machine learning has emerged as a valuable tool for predicting the photocatalytic degradation of medicinal drugs and dyes, providing a promising avenue for addressing urgent demands in removing organic pollutants from wastewater. This research investigates the synergistic application of photocatalysis and machine learning for pollutant degradation, showcasing a sustainable solution with promising effects on environmental remediation and computational efficiency. This study contributes to green chemistry by providing a clever framework for addressing present-day water pollution challenges and achieving era-driven answers.

A review on waste biomass-to-energy: integrated thermochemical and biochemical conversion for resource recovery

Integrating thermochemical–biochemical methods overcomes the single-path limits for bioenergy production. This synergy lowers costs and enhances energy sustainability, highlighting waste-to-energy's vital role in the circular economy transition.