Smart sustainable biorefineries for lignocellulosic biomass

Harnessing Artificial Intelligence for Sustainable Bioenergy: Revolutionizing Optimization, Waste Reduction, and Environmental Sustainability

Assessing the mutual benefits of artificial intelligence (AI) and bioenergy systems, to promote efficient and sustainable energy production. By addressing issues with conventional bioenergy techniques, it highlights how AI is revolutionising optimisation, waste reduction, and environmental sustainability. With its capacity for intelligent decision-making, predictive modelling, and adaptive controls to maximise bioenergy processes, artificial intelligence (AI) emerges as a crucial catalyst for overcoming these obstacles. The focus on particular uses of AI to enhance bioenergy systems. Algorithms for machine learning are essential for forecasting biomass properties, selecting feedstock optimally, and enhancing energy conversion procedures in general. Enhancing real-time adaptability and guaranteeing optimal performance under a range of operational conditions is made possible by the integration of AI-driven monitoring and control systems. Additionally, it looks at how AI supports precision farming methods in bioenergy settings, enhancing crop management strategies and increasing the output of biofuels. AI-guided autonomous systems help with precision planting, harvesting, and processing, which reduces resource use and maximises yield. AI's contribution to advanced biofuel technology by using data analytics and computational models, it can hasten the creation of new, more effective bioenergy sources. AI-driven grid management advancements could guarantee the smooth integration of bioenergy into current energy infrastructures. The revolutionary role that artificial intelligence (AI) has played in bioenergy systems, making a strong case for the incorporation of AI technologies to drive the global energy transition towards a more ecologically conscious and sustainable future.

Biofuel production for circular bioeconomy: Present scenario and future scope

In recent years, biofuel production has attracted considerable attention, especially given the increasing worldwide demand for energy and emissions of greenhouse gases that threaten this planet. In this case, one possible solution is to convert biomass into green and sustainable biofuel, which can enhance the bioeconomy and contribute to sustainable economic development goals. Due to being in large quantities and containing high organic content, various biomass sources such as food waste, textile waste, microalgal waste, agricultural waste and sewage sludge have gained significant attention for biofuel production. Also, biofuel production technologies, including thermochemical processing, anaerobic digestion, fermentation and bioelectrochemical systems, have been extensively reported, which can achieve waste valorization through producing biofuels and re-utilizing wastes. Nevertheless, the commercial feasibility of biofuel production is still being determined, and it is unclear whether biofuel can compete equally with other existing fuels in the market. The concept of a circular economy in biofuel production can promote the environmentally friendly and sustainable valorization of biomass waste. This review comprehensively discusses the state-of-the-art production of biofuel from various biomass sources and the bioeconomy perspectives associated with it. Biofuel production is evaluated within the framework of the bioeconomy. Further perspectives on possible integration approaches to maximizing waste utilization for biofuel production are discussed, and what this could mean for the circular economy. More research related to pretreatment and machine learning of biofuel production should be conducted to optimize the biofuel production process, increase the biofuel yield and make the biofuel prices competitive.

Hotspot Wizard-informed engineering of a hyperthermophilic β-glucosidase for enhanced enzyme activity at low temperatures

Hyperthermophilic enzymes serve as an important source of industrial enzymes due to their high thermostability. Unfortunately, most hyperthermophilic enzymes suffer from reduced activity at low temperatures (e.g., ambient temperature), limiting their applicability. In addition, evolving hyperthermophilic enzymes to increase low temperature activity without compromising other desired properties is generally difficult. In the current study, a variant of β-glucosidase from Pyrococcus furiosus (PfBGL) was engineered to enhance enzyme activity at low temperatures through the construction of a saturation mutagenesis library guided by the HotSpot Wizard analysis, followed by its screening for activity and thermostability. From this library construction and screening, one PfBGL mutant, PfBGL-A4 containing Q214S/A264S/F344I mutations, showed an over twofold increase in β-glucosidase activity at 25 and 50°C compared to the wild type, without compromising high-temperature activity, thermostability and substrate specificity. Our experimental and computational characterizations suggest that the findings with PfBGL-A4 may be due to the elevation of local conformational flexibility around the active site, while slightly compacting the global protein structure. This study showcases the potential of HotSpot Wizard-informed engineering of hyperthermophilic enzymes and underscores the interplays among temperature, enzyme activity, and conformational flexibility in these enzymes.

Caatinga, Amazon and Atlantic Forest as natural sources for microbial lignocellulolytic enzymes

Brazilian biomes are important sources for environmental microorganisms, including efficient metabolic machineries, like actinomycetes. These bacteria are known for their abilities to produce many bioactive compounds, including enzymes with multiple industrial applications. The present work aimed to evaluate lignocellulolytic abilities of actinomycetes isolated from soil and rhizosphere samples collected at Caatinga, Atlantic and Amazon Forest. Laccase (Lac), lignin peroxidase (LiP), manganese peroxidase (MnP) and cellulase were evaluated for their efficiency. These enzymes have an essential role in lignin decomposition, through oxidation of phenolic and non-phenolic compounds, as well as enzymatic hydrolysis of vegetal biomass. In this sense, a total of 173 actinomycetes were investigated. Eleven (11) of them were selected by their enzymatic performance. The actinomycete AC166 displayed some activity in all analysed scenarios in terms of Lac, MnP and LiP activity, while AC171 was selected as the most promising strain, showing the following activities: 29.7 U.L-1 for Lac; 2.5 U.L-1 for LiP and 23 U.L-1 for MnP. Cellulolytic activities were evaluated at two pH conditions, 4.8 and 7.4, obtaining the following results: 25 U.L-1 and 71 U.L-1, respectively. Thermostability (4, 30 and 60 o C) and salinity concentrations (0 to 4 M) and pH variation (2.0 to 9.0) stabilities of the obtained LiP and Lac enzymatic extracts were also verified. The actinomycete strain AC171 displayed an adaptable response in distinct pH and salt profiles, indicating that bacterial LiP was some halophilic type. Additionally, the strain AC149 produced an alkali and extreme halophilic lignin peroxidase, which are promising profiles for their future application under lignocellulosic biomass at bioethanol biorefineries.

A Review of Automation and Sensors: Parameter Control of Thermal Treatments for Electrical Power Generation

This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electrical power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters, such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electrical power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes.

Prediction of composting maturity and identification of critical parameters for green waste compost using machine learning

Ensuring the maturity of green waste compost is crucial to composting processes and quality control of compost products. However, accurate prediction of green waste compost maturity remains a challenge, as there are limited computational methods available. This study aimed to address this issue by employing four machine learning models to predict two indicators of green waste compost maturity: seed germination index (GI) and T value. The four models were compared, and the Extra Trees algorithm exhibited the highest prediction accuracy with R2 values of 0.928 for GI and 0.957 for T value. To identify the interactions between critical parameters and compost maturity, The Pearson correlation matrix and Shapley Additive exPlanations (SHAP) analysis were conducted. Furthermore, the accuracy of the models was validated through compost validation experiments. These findings highlight the potential of applying machine learning algorithms to predict green waste compost maturity and optimise process regulation.

A lignin-derived material improves plant nutrient bioavailability and growth through its metal chelating capacity

The lignocellulosic biorefinery industry can be an important contributor to achieving global carbon net zero goals. However, low valorization of the waste lignin severely limits the sustainability of biorefineries. Using a hydrothermal reaction, we have converted sulfuric acid lignin (SAL) into a water-soluble hydrothermal SAL (HSAL). Here, we show the improvement of HSAL on plant nutrient bioavailability and growth through its metal chelating capacity. We characterize HSAL's high ratio of phenolic hydroxyl groups to methoxy groups and its capacity to chelate metal ions. Application of HSAL significantly promotes root length and plant growth of both monocot and dicot plant species due to improving nutrient bioavailability. The HSAL-mediated increase in iron bioavailability is comparable to the well-known metal chelator ethylenediaminetetraacetic acid. Therefore, HSAL promises to be a sustainable nutrient chelator to provide an attractive avenue for sustainable utilization of the waste lignin from the biorefinery industry.

Intelligent approaches for sustainable management and valorisation of food waste

Food waste (FW) is a severe environmental and social concern that today's civilization is facing. Therefore, it is necessary to have an efficient and sustainable solution for managing FW bioprocessing. Emerging technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) are critical to achieving this, in which IoT sensors' data is analyzed using AI and ML techniques, enabling real-time decision-making and process optimization. This work describes recent developments in valorizing FW using novel tactics such as the IoT, AI, and ML. It could be concluded that combining IoT, AI, and ML approaches could enhance bioprocess monitoring and management for generating value-added products and chemicals from FW, contributing to improving environmental sustainability and food security. Generally, a comprehensive strategy of applying intelligent techniques in conjunction with government backing can minimize FW and maximize the role of FW in the circular economy toward a more sustainable future.

Advances in the catalyzed photo-fermentative biohydrogen production through photo nanocatalysts with the potential of selectivity, and customization

Photo nanocatalyst have shownpromise in a variety of fields, including biohydrogen production where their catalytic efficiency is related to size, surface-to-volume ratio, and increasing the number of atoms on the surface. They can harvest solar light to create electron-hole pairs which is the key mechanism to define its catalytic efficiency, thus requiring suitable excitation wavelength, band energy, and crystal imperfections. In this review, a discussion on the role of photo nanocatalysts to catalyze biohydrogen production has been carried out. Photo nanocatalysts feature a large bandgap, andhigh defect concentration, thus having the ability to be tuned for their characteristics. Customization of the photo nanocatalyst has been addressed. Mechanism of the photo nanocatalysts in catalyzing biohydrogen has been discussed. Limiting factors of photo nanocatalysts were highlighted and several recommendations have been made to enhance the effective utilization of these photo nanocatalysts to enhance photo-fermentative biohydrogen production from biomass wastes.

Development of lignocellulosic biorefineries for the sustainable production of biofuels: Towards circular bioeconomy

The idea of environment friendly and affordable renewable energy resources has prompted the industry to focus on the set up of biorefineries for sustainable bioeconomy. Lignocellulosic biomass (LCB) is considered as an abundantly available renewable feedstock for the production of biofuels which can potentially reduce the dependence on petrochemical refineries. By utilizing various conversion technologies, an integrated biorefinery platform of LCB can be created, embracing the idea of the 'circular bioeconomy'. The development of effective pretreatment methods and biocatalytic systems by various bioengineering and machine learning approaches could reduce the bioprocessing costs, thereby making biomass-based biorefinery more sustainable. This review summarizes the development and advances in the lignocellulosic biorefineries from the LCB to the final product stage using various different state-of-the-art approaches for the progress of circular bioeconomy. The life cycle assessment which generates knowledge on the environmental impacts related to biofuel production chains is also summarized.

Utilization of bamboo as biorefinery feedstock: Co-production of xylo-oligosaccharide with succinic acid and lactic acid

Herein, we investigated the possibility of co-producing xylo-oligosaccharides (XOSs) from bamboo, as value-added products, along with succinic and lactic acids, as platform chemicals. Xylan was extracted from bamboo using the alkali method under mild conditions. From xylan, XOSs were produced by partial enzymatic hydrolysis at a conversion rate of 83.9%, and all reaction conditions resulted in similar degrees of polymerization. Hydrogen peroxide-acetic acid (HPAC) pretreatment effectively removed lignin from NaOH-treated bamboo, and the enzymatic hydrolytic yield of NaOH and HPAC-treated bamboo was 84.3% of the theoretical yield. The production of succinic and lactic acids from the hydrolysate resulted in conversion rates of approximately 63.2% and 91.3% of the theoretical yield using Corynebacterium glutamicum Δldh and Actinobacillus succinogenes, respectively, under facultative anaerobic conditions. This study demonstrates that bamboo has a high potential to produce value-added products using a biorefinery process and is an alternative resource for compounds typically derived from petroleum.

Big data and machine learning driven bioprocessing - Recent trends and critical analysis

Given the potential of machine learning algorithms in revolutionizing the bioengineering field, this paper examined and summarized the literature related to artificial intelligence (AI) in the bioprocessing field. Natural language processing (NLP) was employed to explore the direction of the research domain. All the papers from 2013 to 2022 with specific keywords of bioprocessing using AI were extracted from Scopus and grouped into two five-year periods of 2013-to-2017 and 2018-to-2022, where the past and recent research directions were compared. Based on this procedure, selected sample papers from recent five years were subjected to further review and analysis. The result shows that 50% of the publications in the past five-year focused on topics related to hybrid models, ANN, biopharmaceutical manufacturing, and biorefinery. The summarization and analysis of the outcome indicated that implementing AI could improve the design and process engineering strategies in bioprocessing fields.

Design of biorefineries towards carbon neutrality: A critical review

The increase in worldwide demand for energy is driven by the rapid increase in population and exponential economic development. This resulted in the fast depletion of fossil fuel supplies and unprecedented levels of greenhouse gas in the atmosphere. To valorize biomass into different bioproducts, one of the popular and carbon-neutral alternatives is biorefineries. This system is an appropriate technology in the circular economy model. Various research highlighted the role of biorefineries as a centerpiece in the carbon-neutral ecosystem of technologies of the circular economy model. To fully realize this, various improvements and challenges need to be addressed. This paper presents a critical and timely review of the challenges and future direction of biorefineries as an alternative carbon-neutral energy source.

Optimization of alkali pretreatment and enzymatic saccharification of jute (Corchorus olitorius L.) biomass using response surface methodology

Reduction of inherent structural recalcitrance and improved saccharification efficiency are two important facets to enhance fermentable sugar yield for bioethanol production from lignocellulosic biomass. This study optimized alkaline pretreatment and saccharification conditions employing response surface methodology to improve saccharification yield of jute (Corchorus olitorius cv. JROB-2) biomass. The biomass is composed of cellulose (66.6 %), lignin (19.4 %) and hemicellulose (13.1 %). NaOH concentration exhibited significant effect on delignification during pretreatment. The highest delignification (80.42 %) was obtained by pretreatment with 2.47 % NaOH at 55.8 °C for 5.9 h removing 79.8 % lignin and 34.2 % hemicellulose from biomass, thereby increasing cell wall porosity and allowing better accessibility to saccharification enzyme. During saccharification optimization, significant effect was observed for biomass loading, enzyme concentration and temperature. Optimized saccharification condition yielded maximum saccharification (76.48 %) when hydrolysis was performed at 6.9 % biomass loading with enzyme concentration of 49.52 FPU/g substrate at 51.05 °C for 74.46 h.

Onion waste based-biorefinery for sustainable generation of value-added products

Waste derived from the onion processing sector can be harnessed for the production of organic acids, polyphenols, polysachharides, biofuels and pigments. To sustainably utilize onion processing residues, different biorefinery strategies such as enzymatic hydrolysis, fermentation and hydrothermal carbonization have been widely investigated. This review discusses the recent advances in the biorefinery approaches used for valorization of onion processing waste followed by the production of different value-added products from diverse classes of onion waste. The review also highlights the current challenges faced by the bioprocessing sector for the utilization of onion processing waste and perspectives to tackle them.

Life cycle assessment of microalgal biorefinery: A state-of-the-art review

Microalgal biorefineries represent an opportunity to economically and environmentally justify the production of bioproducts. The generation of bioproducts within a biorefinery system must quantitatively demonstrate its viability in displacing traditional fossil-based refineries. To this end, several works have conducted life cycle analyses on microalgal biorefineries and have shown technological bottlenecks due to energy-intensive processes. This state-of-the-art review covers different studies that examined microalgal biorefineries through life cycle assessments and has identified strategic technologies for the sustainable production of microalgal biofuels through biorefineries. Different metrics were introduced to supplement life cycle assessment studies for the sustainable production of microalgal biofuel. Challenges in the comparison of various life cycle assessment studies were identified, and the future design choices for microalgal biorefineries were established.

Prediction of carbon dioxide production from green waste composting and identification of critical factors using machine learning algorithms

Controlling carbon dioxide produced from green waste composting is a vital issue in response to carbon neutralization. However, there are few computational methods for accurately predicting carbon dioxide production from green waste composting. Based on the data collected, this study developed novel machine learning methods to predict carbon dioxide production from green waste composting and made a comparison among six methods. After eliminating the extreme outliers from the dataset, the Random Forest algorithm achieved the highest prediction accuracy of 88% in the classification task and showed the top performance in the regression task (root mean square error = 23.3). As the most critical factor, total organic carbon, with the Gini index accounting for about 59%, can provide guidance for reducing carbon emissions from green waste composting. These results show that there is great potential for using machine learning algorithms to predict carbon dioxide output from green waste composting.

Corn Cob as a Green Support for Laccase Immobilization-Application on Decolorization of Remazol Brilliant Blue R

The high demand for food and energy imposed by the increased life expectancy of the population has driven agricultural activity, which is reflected in the larger quantities of agro-industrial waste generated, and requires new forms of use. Brazil has the greatest biodiversity in the world, where corn is one of the main agricultural genres, and where over 40% of the waste generated is from cobs without an efficient destination. With the aim of the valorization of these residues, we proposed to study the immobilization of laccase from Aspergillus spp. (L_Asp) in residual corn cob and its application in the degradation of Remazol Brilliant Blue R (RBBR) dye. The highest yields in immobilized protein (75%) and residual activity (40%) were obtained at pH 7.0 and an enzyme concentration of 0.1 g.mL⁻¹, whose expressed enzyme activity was 1854 U.kg⁻¹. At a temperature of 60 °C, more than 90% of the initial activity present in the immobilized biocatalyst was maintained. The immobilized enzyme showed higher efficiency in the degradation (64%) of RBBR dye in 48 h, with improvement in the process in 72 h (75%). The new biocatalyst showed operational efficiency during three cycles, and a higher degradation rate than the free enzyme, making it a competitive biocatalyst and amenable to industrial applications.

Rapid evolution and mechanism elucidation for efficient cellobiose-utilizing Saccharomyces cerevisiae through Synthetic Chromosome Rearrangement and Modification by LoxPsym-mediated Evolution

Lack of cellobiose utilization capability for many microorganisms results in carbon source waste in lignocellulosic biorefinery. In this study, genes for cellobiose transport and hydrolysis were introduced to Saccharomyces cerevisiae synV, a semi-synthetic yeast with an inducible SCRaMbLE (Synthetic Chromosome Rearrangement and Modification by LoxPsym-mediated Evolution) system incorporated into its chromosome V, endowing cellobiose utilization capability to this strain. Thereafter, two evolved strains with 98.1% and 79.2% improvement, respectively, in cellobiose utilization rate were obtained through induced SCRaMbLE. Further studies suggested that the enhanced cellobiose utilization capability directly correlated with copy number increases of introduced genes and some chromosome structural variations. In particular, it was experimentally demonstrated for the first time that deletion of redox stress related gene MXR1 and ATP conversion related gene ADK2 contributed to enhanced cellobiose conversion. Thereafter, the effectiveness of MXR1 and ADK2 deletions was demonstrated in artificial hydrolysate and rice straw hydrolysate, respectively.

Performance Evaluation of Combined Hydrothermal-Mechanical Pretreatment of Lignocellulosic Biomass for Enzymatic Enhancement

Pretreatment is a crucial process in a lignocellulosic biorefinery. Corncob is typically considered as a natural renewable carbon source to produce various bio-based products. This study aimed to evaluate the performance of the hydrothermal-mechanical pretreatment of corncob for biofuels and biochemical production. Corncob was first pretreated by liquid hot water (LHW) at different temperatures (140–180 °C) and duration (30, 60 min) and then subjected to centrifugal milling to produce bio-powders. To evaluate the performance of this combined pretreatment, the energy efficiency and waste generation were investigated. The results indicated that the maximum fermentable sugars (FS) were 0.488 g/g biomass obtained by LHW at 180 °C, 30 min. In order to evaluate the performance of this combined pretreatment, the energy efficiency and waste generation were 28.3 g of FS/kWh and 7.21 kg of waste/kg FS, respectively. These obtained results indicate that the combined hydrothermal-mechanical pretreatment was an effective pretreatment process to provide high energy efficiency and low waste generation to produce biofuels. In addition, the energy efficiency and waste generation will be useful indicators for process scaling-up into the industrial scale. This combined pretreatment could be a promising pretreatment technology for the production of biofuels and biochemicals from lignocellulosic valorization.

Consolidated microbial production of four-, five-, and six-carbon organic acids from crop residues: Current status and perspectives

The production of platform organic acids has been heavily dependent on petroleum-based industries. However, petrochemical-based industries that cannot guarantee a virtuous cycle of carbons released during various processes are now facing obsolescence because of the depletion of finite fossil fuel reserves and associated environmental pollutions. Thus, the transition into a circular economy in terms of the carbon footprint has been evaluated with the development of efficient microbial cell factories using renewable feedstocks. Herein, the recent progress on bio-based production of organic acids with four-, five-, and six-carbon backbones, including butyric acid and 3-hydroxybutyric acid (C4), 5-aminolevulinic acid and citramalic acid (C5), and hexanoic acid (C6), is discussed. Then, the current research on the production of C4-C6 organic acids is illustrated to suggest future directions for developing crop-residue based consolidated bioprocessing of C4-C6 organic acids using host strains with tailor-made capabilities.

An Experimental Study on the Hot Alkali Extraction of Xylan-Based Hemicelluloses from Wheat Straw and Corn Stalks and Optimization Methods

In this paper, we describe an experimental study on the hot alkali extraction of hemicelluloses from wheat straw and corn stalks, two of the most common lignocellulosic biomass constituents in Romania. The chemical compositions of the raw materials were determined analytically, and the relevant chemical components were cellulose, hemicelluloses, lignin, and ash. Using the response surface methodology, the optimum values of the hot alkaline extraction parameters, i.e., time, temperature, and NaOH concentration, were identified and experimentally validated. The physicochemical characterization of the isolated hemicelluloses was performed using HPLC, FTIR, TG, DTG, and 1H-NMR spectroscopy. The main hemicellulose components identified experimentally were xylan, arabinan, and glucan. The study emphasizes that both corn stalks and wheat straw are suitable as raw materials for hemicellulose extraction, highlighting the advantages of alkaline pretreatments and showing that optimization methods can further improve the process efficiency.

Multivariable advanced nonlinear controller for bioethanol production in a non-isothermal fermentation bioreactor

Design for fermentation bioreactor controllers is challenged by the nonlinear process kinetics and the lack of online measurements for key variables. This work developed a multi-input, multi-output advanced nonlinear control structure for a continuous, non-isothermal, constant volume fermentation bioreactor. Utilizing feedback linearization control for the bioreactor feed to regulate glucose concentration, and backstepping control for the cooling jacket feed to regulate reactor temperature. A developed novel estimator for biomass concentration was incorporated to provide online estimates for the unmeasurable state variable. Simulation results showed the control structure ability in efficiently establishing a combination of dynamic and fixed set points, despite disturbances in the bioreactor feed temperature and glucose concentration. Expanded bioreactor control authority increased operational flexibility and enhanced the potential for performance improvements. This work illustrated the effectiveness of feedback linearization and backstepping control in designing controllers for biological systems with nonlinear dynamics, complex interactions, and input disturbances.