Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Abstract

Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐scale applications of biomass‐based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005 and 2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy‐related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end‐use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end‐uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy‐related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives.

Modeling and Optimizing N/O-Enriched Bio-Derived Adsorbents for CO2 Capture: Machine Learning and DFT Calculation Approaches

“…Fundamentally, ANN is inspired by the biological thought route along with the interlinked neurons used to describe the extensive as-prepared data sets. Also, ANN can be established in the procedure on a large number of data sets and variables by adjusting the connection’s values and generalization assessments. , Before running the ANN training procedure in the ML model (RBF-NN), the extracted data sets were normalized in the range of [0, 1]. The normalization process is linearly applied by mapping the data sets over a specified range, and then each value of variable “ x ” is defined as follows where x and x n correspond to the original data and the normalized independent and dependent variables values, respectively.…”

“…Also, ANN can be established in the procedure on a large number of data sets and variables by adjusting the connection's values and generalization assessments. 100,101 Before running the ANN training procedure in the ML model (RBF-NN), the extracted data sets were normalized in the range of [0, 1]. The normalization process is linearly applied by mapping the data sets over a specified range, and then each value of variable "x" is defined as follows…”

Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework

“…The other three studies for supply chain used different indicators such as net present value (Pozo et al, 2012), expected profit (Ehrenstein et al, 2019;Guillén et al, 2006), and the average profit for scenarios with different supplier disruptions (Behdani et al, 2012(Behdani et al, , 2019, and other self-defined profit function (Cao et al, 2018). Note that this review does not include AI studies for bio-based chemicals as they have not been widely commercialized (Sharma et al, 2013), and readers are referred to a recent review of AI applications to biorefineries for more details (Liao & Yao, 2021).…”

“…Liu et al (2020) reviewed 36 studies that applied big data analytics to GSDM, and pointed out a few of them that have applied AI to green purchasing (Liu et al, 2020), although none of these are related to the chemical industry. Liao and Yao (2021) reviewed AI applications in the bioenergy field and discussed the potential benefits of AI in addressing computational barriers by providing near-optimal solutions for supply chain design problems (Liao & Yao, 2021).…”

A Review on Machine Learning Application in Biodiesel Production Studies

“…AI is the ability of machines to simulate the human brain activities, applied through different computer science techniques, like heuristic algorithms, machine learning, and fuzzy logic [45][46][47]. It is chiefly employed to predict biomass and biofuel properties, bioenergy end-use systems performance, conversion process performance, supply chain modeling, and optimization.…”