The effect of process parameters on kinetics and mechanisms of Co2+ removal by bone char

Biochar as environmental armour and its diverse role towards protecting soil, water and air

Biochar has been of considerable importance for various environmental applications in recent years. It has exhibited substantial advantages like favourable structural and surface properties, easy process of preparation and widely available feedstocks. These set of exceptional properties make it an efficient, cost-effective and environment friendly source for diversified elimination of pollutants. The heterogeneity of physico-chemical properties offers a possibility for biochar to optimize its efficacy for targeted applications. This review aims to highlight the critical role that biochar plays in various environmental applications, be it in soil, water or air. In particular the article offers a comprehensive review of the recent research findings and updates related to the diversified role of biochar. Also, the interaction of pollutants with biochar functional groups and the impact of variation of parameters on biochar attribute relevant to specific pollutant removal, modifications, mechanisms involved and competence for such removal has been discussed. Different technologies for production of biochar have also been summarized with an emphasis on post treatment of biochar, such as modification and doping. In addition to this, the underlying gaps in the studies carried out so far and recommendations for future research areas in biochar have also been deliberated.

A review of biochar-based sorbents for separation of heavy metals from water

Biochar is the low-cost and environmental-friendly material which has shown a great potential for separation of heavy metals from water. The previous studies have established a superior role of biochar over other materials, such as activated carbon and inorganic sorbents (iron based) in efficient removal of toxic heavy metals from aqueous systems. Among the various factors influencing heavy metals sorption ability of biochar, types of feedstock/biomass and pyrolysis temperature play a significant role. The goal of this review is to increase our understanding of heavy metals sorption behavior by biochars - this is important as heavy metals sorption is driven based on biochar type, heavy metals species which involve numerous mechanisms, including the physical binding, complexation, ion exchange, surface precipitation and electrostatic interactions. In addition, this review paper describes various approaches to improve heavy metal sorption capacity of biochars by steam and acids/bases activations and impregnation of biochar-based composites with minerals, organic compounds and carbon-rich materials. The physical/chemical activation of biochars can improve the surface area, thus leading to their improved functionality, while modification/pretreatment methods help in synthesizing composites using biochar as a supporting media to develop new sorbents with efficient surface attributes for heavy metals removal from aqueous solutions.

Modeling Pb (II) adsorption from aqueous solution by ostrich bone ash using adaptive neural-based fuzzy inference system

To evaluate the performance of Adaptive Neural-Based Fuzzy Inference System (ANFIS) model in estimating the efficiency of Pb (II) ions removal from aqueous solution by ostrich bone ash, a batch experiment was conducted. Five operational parameters including adsorbent dosage (C(s)), initial concentration of Pb (II) ions (C(o)), initial pH, temperature (T) and contact time (t) were taken as the input data and the adsorption efficiency (AE) of bone ash as the output. Based on the 31 different structures, 5 ANFIS models were tested against the measured adsorption efficiency to assess the accuracy of each model. The results showed that ANFIS5, which used all input parameters, was the most accurate (RMSE = 2.65 and R(2) = 0.95) and ANFIS1, which used only the contact time input, was the worst (RMSE = 14.56 and R(2) = 0.46). In ranking the models, ANFIS4, ANFIS3 and ANFIS2 ranked second, third and fourth, respectively. The sensitivity analysis revealed that the estimated AE is more sensitive to the contact time, followed by pH, initial concentration of Pb (II) ions, adsorbent dosage, and temperature. The results showed that all ANFIS models overestimated the AE. In general, this study confirmed the capabilities of ANFIS model as an effective tool for estimation of AE.