Machine learning and statistical analysis for biomass torrefaction: A review

Deterministic Models for Performance Analysis of Lignocellulosic Biomass Torrefaction

Energy plays a key role in the socioeconomic development of society, and most of its global demand is provided by conventional resources (e.g., fossil fuels). Utilizing renewable energy is significantly growing since it can meet global energy demand while minimizing the adverse impacts of carbon emissions on climate change. Biomass is an appealing option among the emerging alternatives (e.g., wind and solar). Torrefaction is a mild pyrolysis process, and this research aims to analyze the torrefaction process of lignocellulosic biomass. The methodology proposed involves employing hybrid models of artificial neural network-particle swarm optimization (ANN-PSO), adaptive neuro-fuzzy inference system (ANFIS), and coupled simulated annealing-least-squares support vector machine (CSA-LSSVM). In addition to the machine learning algorithms, a correlation is developed using gene expression programming (GEP) to interrelate the biomass properties, including moisture content, volatile matter, fixed carbon, ash, sample size, and the contents of oxygen, carbon, hydrogen, and nitrogen along with the process operating condition encompassing residence time, temperature, and the concentration of CO2, O2, and N2 to the solid yield as the target variable. The results reveal that the CSA-LSSVM model has the highest accuracy, and the statistical metrics of the coefficient of determination (R 2), mean square error (MSE), and average absolute relative error percentage (AARE%) are 0.98, 0.00082, and 2.61%, respectively. The parametric sensitivity analysis demonstrates the residence time, temperature, and moisture content as the most influential variables, with temperature playing the most crucial role in the torrefaction process of lignocellulosic biomass. The findings and the developed models can be used to assess similar biomass torrefaction, providing the required knowledge for the modeling and optimization of the process. Hence, the bioenergy industry can be developed with optimal operating conditions, including cost and energy, and lessen the negative impacts of CO2 emission.

Simulation and Thermodynamic Evaluation of Woody Biomass Waste Torrefaction

Torrefaction is a thermochemical pretreatment that enhances biomass properties, improving energy density, decomposition resistance, and hydrophobicity, making it a viable alternative as biofuel. This study performed a thermodynamic assessment of the torrefaction process for urban forest waste, integrating experimental data with two-step reaction kinetic modeling to evaluate the torrefaction product yields and properties using Aspen Plus software. The process was modeled with a yield reactor, employing the Peng-Robinson equation to describe vapor-phase behavior and empirical correlations to predict solid-phase properties. Simulations were validated against experimental data for temperatures between 225 and 275 °C, achieving an absolute deviation of less than 5%. Energy consumption ranged from 368 kJ·h-1 for light torrefaction to 1853 kJ·h-1 for severe torrefaction. Process irreversibility varied from 326 kJ·h-1 (3% exergy destruction) in light torrefaction to 3993 kJ·h-1 (16% exergy destruction) in severe torrefaction. The research provides a robust model for torrefaction scale-up that is adaptable to diverse biomass feedstocks and process conditions, highlighting its potential for optimizing energy use and improving sustainability in biomass utilization.

Machine learning-based exploration of biochar for environmental management and remediation

Biochar has a wide range of applications, including environmental management, such as preventing soil and water pollution, removing heavy metals from water sources, and reducing air pollution. However, there are several challenges associated with the usage of biochar for these purposes, resulting in an abundance of experimental data in the literature. Accordingly, the purpose of this study is to examine the use of machine learning in biochar processes with an eye toward the potential of biochar in environmental remediation. First, recent developments in biochar utilization for the environment are summarized. Then, a bibliometric analysis is carried out to illustrate the major trends (demonstrating that the top three keywords are heavy metal, wastewater, and adsorption) and construct a comprehensive perspective for future studies. This is followed by a detailed review of machine learning applications, which reveals that adsorption efficiency and capacity are the primary utilization targets in biochar utilization. Finally, a comprehensive perspective is provided for the future. It is then concluded that machine learning can help to detect hidden patterns and make accurate predictions for determining the combination of variables that results in the desired properties which can be later used for decision-making, resource allocation, and environmental management.

Artificial intelligence methods for modeling gasification of waste biomass: a review

Gasification is a highly promising thermochemical process that shows considerable potential for the efficient conversion of waste biomass into syngas. The assessment of the feasibility and comparative advantages of different biomass and waste gasification schemes is contingent upon a multifaceted combination of interrelated criteria. Conventional analytical approaches employed to facilitate decision-making rely on a multitude of inadequately defined parameters. Consequently, substantial efforts have been directed toward enhancing the efficiency and productivity of thermochemical conversion processes. In recent times, artificial intelligence (AI)-based models and algorithms have gained prominence, serving as indispensable tools for expediting these processes and formulating strategies to address the growing demand for energy. Notably, machine learning (ML) and deep learning (DL) have emerged as cutting-edge AI models, demonstrating exceptional effectiveness and profound relevance in the realm of thermochemical conversion systems. This study provides an overview of the machine learning (ML) and deep learning (DL) approaches utilized during gasification and evaluates their benefits and drawbacks. Many industries and applications related to energy conversion systems use AI algorithms. Predicting the output of conversion systems and subjects linked to optimization are two of this science's critical applications. This review sheds light on the burgeoning utility of AI, particularly ML and DL, which have garnered significant attention due to their applications in productivity prediction, process optimization, real-time process monitoring, and control. Furthermore, the integration of hybrid models has become commonplace, primarily owing to their demonstrated success in modeling and optimization tasks. Importantly, the adoption of these algorithms significantly enhances the model's capability to tackle intricate challenges, as DL methodologies have evolved to offer heightened accuracy and reduced susceptibility to errors. Within the scope of this study, an exhaustive exploration of ML and DL techniques and their applications has been conducted, uncovering existing research knowledge gaps. Based on a comprehensive critical analysis, this review offers recommendations for future research directions, accentuating the pivotal findings and conclusions derived from the study.

Machine learning applications for biochar studies: A mini-review

Biochar is a promising carbon sink whose application can assist in reducing carbon emissions. Development of this technology currently relies on experimental trials, which are time-consuming and labor-intensive. Machine learning (ML) technology presents a potential solution for streamlining this process. This review summarizes the current research on ML's applications in biochar production, characterization, and applications. It briefly explains commonly used machine learning algorithms and discusses prospects and challenges. A hybrid model that combines ML with mechanism-based analysis could be a future trend, addressing the ML's black-box nature. While biochar studies have adopted ML technology, current works mostly use lab-scale data for model training. Further work is needed to develop ML models based on pilot or industrial-scale data to realize the use of ML techniques for the field application of biochar.

Machine learning for municipal sludge recycling by thermochemical conversion towards sustainability

The sustainable disposal of high-moisture municipal sludge (MS) has received increasing attention. Thermochemical conversion technologies can be used to recycle MS into liquid/gas bio-fuel and value-added solid products. In this review, we compared energy recovery potential of common thermochemical technologies (i.e., incineration, pyrolysis, hydrothermal conversion) for MS disposal via statistical methods, which indicated that hydrothermal conversion had a great potential in achieving energy recovery from MS. The application of machine learning (ML) in MS recycling was discussed to decipher complex relationships among MS components, process parameters and physicochemical reactions. Comprehensive ML models should be developed considering successive reaction processes of thermochemical conversion in future studies. Furthermore, challenges and prospects were proposed to improve effectiveness of ML for energizing thermochemical conversion of MS regarding data collection and preprocessing, model optimization and interpretability. This review sheds light on mechanism exploration of MS thermochemical recycling by ML, and provide practical guidance for MS recycling.

In situ reductive dehalogenation of groundwater driven by innovative organic carbon source materials: Insights into the organohalide-respiratory electron transport chain

In situ bioremediation using organohalide-respiring bacteria (OHRB) is a prospective method for the removal of persistent halogenated organic pollutants from groundwater, as OHRB can utilize H₂ or organic compounds produced by carbon source materials as electron donors for cell growth through organohalide respiration. However, few previous studies have determined the suitability of different carbon source materials to the metabolic mechanism of reductive dehalogenation from the perspective of electron transfer. The focus of this critical review was to reveal the interactions and relationships between carbon source materials and functional microbes, in terms of the electron transfer mechanism. Furthermore, this review illustrates some innovative strategies that have used the physiological characteristics of OHRB to guide the optimization of carbon source materials, improving the abundance of indigenous dehalogenated bacteria and enhancing electron transfer efficiency. Finally, it is proposed that future research should combine multi-omics analysis with machine learning (ML) to guide the design of effective carbon source materials and optimize current dehalogenation bioremediation strategies to reduce the cost and footprint of practical groundwater bioremediation applications.

Artificial intelligence and machine learning for smart bioprocesses

In recent years, the digital transformation of bioprocesses, which focuses on interconnectivity, online monitoring, process automation, artificial intelligence (AI) and machine learning (ML), and real-time data acquisition, has gained considerable attention. AI can systematically analyze and forecast high-dimensional data obtained from the operating dynamics of bioprocess, allowing for precise control and synchronization of the process to improve performance and efficiency. Data-driven bioprocessing is a promising technology for tackling emerging challenges in bioprocesses, such as resource availability, parameter dimensionality, nonlinearity, risk mitigation, and complex metabolisms. This special issue entitled "Machine Learning for Smart Bioprocesses (MLSB-2022)" was conceptualized to incorporate some of the recent advances in applications of emerging tools such as ML and AI in bioprocesses. This VSI: MLSB-2022 contains 23 manuscripts, and summarizes the major findings that can serve as a valuable resource for researchers to learn major advances in applications of ML and AI in bioprocesses.