Neural Network Modeling of Heavy Metal Sorption on Lignocellulosic Biomasses: Effect of Metallic Ion Properties and Sorbent Characteristics

Application of biochar for minewater remediation: Effect of scaling up production on performance under laboratory and field conditions

Metals discharged from abandoned mines are a major source of pollution in many parts of the world. As a result, there is a growing need for suitable low-cost remediation methods. While a large literature base exists demonstrating the efficacy of biochar to remove metals from solution, most studies are confined to the laboratory. This study examines the effects on the biochar quality when scaling up production from laboratory to pilot scale. Pilot scale biochars were produced using a 600 kg batch pyrolysis reactor, these chars were then deployed in the field using a series of 100 mm × 1200 mm cylindrical treatment cells installed at the point of discharge from an abandoned mine site. Most biochars produced at a pilot removed more zinc under laboratory conditions, however all of the biochars showed a reduced performance when tested in the field, this ranged from a 14% to an 85% reduction depending on the biochar.

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.

Smart sustainable biorefineries for lignocellulosic biomass

Lignocellulosic biomass (LCB) is considered as a sustainable feedstock for a biorefinery to generate biofuels and other bio-chemicals. However, commercialization is one of the challenges that limits cost-effective operation of conventional LCB biorefinery. This article highlights some studies on the sustainability of LCB in terms of cost-competitiveness and environmental impact reduction. In addition, the development of computational intelligence methods such as Artificial Intelligence (AI) as a tool to aid the improvement of LCB biorefinery in terms of optimization, prediction, classification, and decision support systems. Lastly, this review examines the possible research gaps on the production and valorization in a smart sustainable biorefinery towards circular economy.

Application of neural network in metal adsorption using biomaterials (BMs): a review

ANN models for predicting wastewater treatment efficacy of biomaterial adsorbents.

Taguchi design-based enhancement of heavy metals bioremoval by agroindustrial waste biomass from artichoke

The Taguchi method of designing experiments is based on a system of tabulated designs (arrays) that enables the maximum number of variables to be estimated in a neutral (orthogonal) balanced manner with a minimum number of experimental sets. Heavy metals remediation of aqueous streams is of special concern due to its highly toxic and persistent nature. Taguchi approach was used for enhanced bioadsorptive removal of Pb(II), Cu(II) and Cd(II) from aqueous solutions using agroindustrial waste biomass from globe artichoke as inexpensive sorbent. Sorbent biomass was characterized as to its chemical composition by infrared spectroscopy (FTIR), revealing the presence of hydroxyl, carboxyl, sulphonic and amine functional groups. Ranks of four factors (pH, temperature, sorbent dosage and initial metal concentration) at three levels each, in a L9 array were conducted, in batch sorption tests, for the individual metal ions of concern. The sorption capacity (q_e) values were transformed into an accurate signal-to-noise (S/N) ratio for a "higher is better" response. The best conditions for individual heavy metal sorption were determined reaching up to 86.2 mg·g^-1 for Pb, 35.8 mg·g^-1 for Cd and 24.4 mg·g^-1 for Cu. This paper also discusses the equilibria and kinetic aspects of the sorption process. Sorption isotherms were successfully described by the Sips model. In addition, the experimental data showed that the uptake kinetic profiles of the three metal ions closely fitted the pseudo-second order model. Conclusively, the agroindustrial waste biomass from globe artichoke represents a potentially viable sorbent for the bioremoval of Pb(II), Cu(II) and Cd(II) ions from aqueous systems.

A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence

Water pollution occurs mainly due to inorganic and organic pollutants, such as nutrients, heavy metals and persistent organic pollutants. For the modeling and optimization of pollutants removal, artificial intelligence (AI) has been used as a major tool in the experimental design that can generate the optimal operational variables, since AI has recently gained a tremendous advance. The present review describes the fundamentals, advantages and limitations of AI tools. Artificial neural networks (ANNs) are the AI tools frequently adopted to predict the pollutants removal processes because of their capabilities of self-learning and self-adapting, while genetic algorithm (GA) and particle swarm optimization (PSO) are also useful AI methodologies in efficient search for the global optima. This article summarizes the modeling and optimization of pollutants removal processes in water treatment by using multilayer perception, fuzzy neural, radial basis function and self-organizing map networks. Furthermore, the results conclude that the hybrid models of ANNs with GA and PSO can be successfully applied in water treatment with satisfactory accuracies. Finally, the limitations of current AI tools and their new developments are also highlighted for prospective applications in the environmental protection.