Process systems engineering for biorefineries: new research vistas

Mechanisms, methods and applications of machine learning in bio-alcohol production and utilization: A review

Bio-alcohols have been proven promising alternatives to fossil fuels. Machine learning (ML), as an analytical tool for uncovering intrinsic correlations and mining data connotations, is also becoming widely used in the field of bio-alcohols. This article reviews the mechanisms, methods, and applications of ML in the bio-alcohols field. In terms of mechanisms, we describe the workflow of ML applications, emphasizing the importance of a well-defined research problem and complete feature engineering for a robust model. Prediction and optimization are the main application scenarios. In terms of methods, we illustrate the characteristics of different ML models and analyze their applicability in the bio-alcohol field. The role of ML in the production of bio-methanol by pyrolysis and gasification, as well as in the three stages of fermentation for bioethanol production are highlighted. In terms of utilization, ML is used to optimize engine performance and reduce emissions. This review provides guidance on how to use novel ML methods in the bio-alcohol field, showing the potential of ML to streamline work in the whole biofuel field.

The role of bioprocess systems engineering in extracting chemicals and energy from microalgae

In this study, the role of process systems engineering in enhancing the algae economy is highlighted. First, basic characteristics of the various strains of microalgae are presented. In addition, the beneficial extracted bioproducts and their applications are reviewed. Then, an overview of the various technologies available in each step of biorefinery to produce added-value products and biofuels from microalgae is provided. These technologies are compared in terms of required energy and efficiency. Different perspectives of the algae industry, from molecule to enterprises scale where process systems engineering can have a role, are addressed. Subsequently, the roles of process systems engineering in process and product design, process control, and supply chain of the algae biorefinery are discussed. It is found that process systems engineering can play an important role in the biobased economy, especially by applying sustainability and economic concepts in the decision-making process for selecting the best feedstock, processing pathways, and desired products. Tools such as market analysis, techno-economic analysis, life cycle assessment (LCA), and supply chain (SC) analysis can be applied to design sustainable algae biorefinery. There are, however, several challenges such as the lack of data, the complexity of optimization, and validation that should be addressed before using these tools.

Multicriteria Definition of Small-Scale Biorefineries Based on a Statistical Classification

Biorefineries have many possible designs and therefore, present varied benefits in regards to sustainable development. Evaluating these biorefineries is central for the domain, and, as small-scale biorefineries (SSB) are commonly opposed to the large ones, specifying the concept of scale of a biorefinery is essential as well. However, there is no consensual definition of the “scale”, and the meaning of the term changes with the context. This paper presents a methodology to specify the concept of scale by grouping various biorefineries processing lignocellulosic biomass according to factors related to feedstock, process, economy and mobility of the facility, without any predetermined pattern. Data from 15 operational biorefineries are analyzed using a multivariate analysis combined with a hierarchical clustering. The classification obtained categorizes biorefineries into four design classes: smallest, small, hybrid and large scale. Small-scale biorefineries are characterized by a small investment cost (less than 2 M€), a low processing capacity (less than 100 t/day) and a low process complexity, while the end-products’ added value is variable. The mobility of the plants is a sufficient, but not necessary, criterion to have a small-scale biorefinery. Finally, the designs of the investigated biorefineries can be explained by two main trade-offs: one between the mobility and the processing capacity-investment cost, and the other between the process complexity and the added value.

Fermentative Conversion of Two-Step Pre-Treated Lignocellulosic Biomass to Hydrogen

Fermentative hydrogen production via dark fermentation with the application of lignocellulosic biomass requires a multistep pre-treatment procedure, due to the complexed structure of the raw material. Hence, the comparison of the hydrogen productivity potential of different lignocellulosic materials (LCMs) in relation to the lignocellulosic biomass composition is often considered as an interesting field of research. In this study, several types of biomass, representing woods, cereals and grass were processed by means of mechanical pre-treatment and alkaline and enzymatic hydrolysis. Hydrolysates were used in fermentative hydrogen production via dark fermentation process with Enterobacter aerogenes (model organism). The differences in the hydrogen productivity regarding different materials hydrolysates were analyzed using chemometric methods with respect to a wide dataset collected throughout this study. Hydrogen formation, as expected, was positively correlated with glucose concentration and total reducing sugars amount (YTRS) in enzymatic hydrolysates of LCMs, and negatively correlated with concentrations of enzymatic inhibitors i.e., HMF, furfural and total phenolic compounds in alkaline-hydrolysates LCMs, respectively. Interestingly, high hydrogen productivity was positively correlated with lignin content in raw LCMs and smaller mass loss of LCM after pre-treatment step. Besides results of chemometric analysis, the presented data analysis seems to confirm that the structure and chemical composition of lignin and hemicellulose present in the lignocellulosic material is more important to design the process of its bioconversion than the proportion between the cellulose, hemicellulose and lignin content in this material. For analyzed LCMs we found remarkable higher potential of hydrogen production via bioconversion process of woods i.e., beech (24.01 mL H2/g biomass), energetic poplar (23.41 mL H2/g biomass) or energetic willow (25.44 mL H2/g biomass) than for cereals i.e., triticale (17.82 mL H2/g biomass) and corn (14.37 mL H2/g biomass) or for meadow grass (7.22 mL H2/g biomass).

Knowledge management in a waste based biorefinery in the QbD paradigm

Shifting resource base from fossil feedstock to renewable raw materials for production of chemical products has opened up an area of novel applications of industrial biotechnology-based process tools. This review aims to provide a concise and focused discussion on recent advances in knowledge management to facilitate efficient and optimal operation of a biorefinery. Application of quality by design (QbD) and process analytical technology (PAT) as tools for knowledge creation and management at different levels has been highlighted. Role of process integration, government policies, knowledge exchange through collaboration, and use of databases and computational tools have also been touched upon.

Metagenomics for the development of new biocatalysts to advance lignocellulose saccharification for bioeconomic development

The world economy is moving toward the use of renewable and nonedible lignocellulosic biomasses as substitutes for fossil sources in order to decrease the environmental impact of manufacturing processes and overcome the conflict with food production. Enzymatic hydrolysis of the feedstock is a key technology for bio-based chemical production, and the identification of novel, less expensive and more efficient biocatalysts is one of the main challenges. As the genomic era has shown that only a few microorganisms can be cultured under standard laboratory conditions, the extraction and analysis of genetic material directly from environmental samples, termed metagenomics, is a promising way to overcome this bottleneck. Two screening methodologies can be used on metagenomic material: the function-driven approach of expression libraries and sequence-driven analysis based on gene homology. Both techniques have been shown to be useful for the discovery of novel biocatalysts for lignocellulose conversion, and they enabled identification of several (hemi)cellulases and accessory enzymes involved in (hemi)cellulose hydrolysis. This review summarizes the latest progress in metagenomics aimed at discovering new enzymes for lignocellulose saccharification.