Production of biofuels from biomass: Predicting the energy employing artificial intelligence modelling

Artificial intelligence in plant breeding

Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.

Design of a sustainable supply chain network of biomass renewable energy in the case of disruption

Non-renewable energy sources, including fossil fuels, are a type of energy whose consumption rate far exceeds its natural production rate. Therefore, non-renewable resources will be exhausted if alternative energy is not fully developed, leading to an energy crisis in the near future. In this paper, a mathematical model has been proposed for the design of the biomass supply chain of field residues that includes several fields where residue is transferred to hubs after collecting the residue in the hub, the residue is transferred to reactors. In reactors, the residue is converted into gas, which is transferred to condenser and transformers, converted into electricity and sent to demand points through the network. In this paper, the criteria of stability and disturbance were considered, which have been less discussed in related research, and the purpose of the proposed model was to maximize the profit from the sale of energy, including the selling price minus the costs. Genetic algorithm (GA) and simulated annealing (SA) algorithm have been used to solve the model. Then, to prove the complexity of the problem, different and random examples have been presented in different dimensions of the problem. Also, the efficiency of the algorithm in small and large dimensions was proved by comparing GA and SA due to the low deviation of the solutions and the methods used have provided acceptable results suitable for all decision-makers. Also, the effectiveness of the algorithm in small and large dimensions is proven by comparing the genetic algorithm and simulated annealing, and the genetic algorithm's values are better, considering the deviation of 2.9%.and have provided solution methods suitable for all decision makers.

The adsorption routes of 4IR technologies for effective desulphurization using cellulose nanocrystals: Current trends, challenges, and future perspectives

The combustion of liquid fuels as energy sources for transportation and power generation has necessitated governments worldwide to direct petroleum refineries to produce sulphur-free fuels for environmental sustainability. This review highlights the novel application of artificial intelligence for optimizing and predicting adsorptive desulphurization operating parameters and green isolation conditions of nanocellulose crystals from lignocellulosic biomass waste. The shortcomings of the traditional modelling and optimization techniques are stated, and artificial intelligence's role in overcoming them is broadly discussed. Also, the relationship between nanotechnology and artificial intelligence and the future perspectives of fourth industrial revolution (4IR) technologies for optimization and modelling of the adsorptive desulphurization process are elaborately discussed. The current study surveys different adsorbents used in adsorptive desulphurization and how biomass-based nanocellulose crystals (green adsorbents) are suitable alternatives for achieving cleaner fuels and environmental sustainability. Likewise, the present study reports the challenges and potential solutions to fully implementing 4IR technologies for effective desulphurization of liquid fuels in petroleum refineries. Hence, this study provides insightful information to benefit a broad audience in waste valorization for sustainability, environmental protection, and clean energy generation.

Harnessing the potential of microalgae-bacteria interaction for eco-friendly wastewater treatment: A review on new strategies involving machine learning and artificial intelligence

In the pursuit of effective wastewater treatment and biomass generation, the symbiotic relationship between microalgae and bacteria emerges as a promising avenue. This analysis delves into recent advancements concerning the utilization of microalgae-bacteria consortia for wastewater treatment and biomass production. It examines multiple facets of this symbiosis, encompassing the judicious selection of suitable strains, optimal culture conditions, appropriate media, and operational parameters. Moreover, the exploration extends to contrasting closed and open bioreactor systems for fostering microalgae-bacteria consortia, elucidating the inherent merits and constraints of each methodology. Notably, the untapped potential of co-cultivation with diverse microorganisms, including yeast, fungi, and various microalgae species, to augment biomass output. In this context, artificial intelligence (AI) and machine learning (ML) stand out as transformative catalysts. By addressing intricate challenges in wastewater treatment and microalgae-bacteria symbiosis, AI and ML foster innovative technological solutions. These cutting-edge technologies play a pivotal role in optimizing wastewater treatment processes, enhancing biomass yield, and facilitating real-time monitoring. The synergistic integration of AI and ML instills a novel dimension, propelling the fields towards sustainable solutions. As AI and ML become integral tools in wastewater treatment and symbiotic microorganism cultivation, novel strategies emerge that harness their potential to overcome intricate challenges and revolutionize the domain.

Machine Learning Approach for Determining and Optimizing Influential Factors of Biogas Production from Lignocellulosic Biomass

Machine learning (ML) was used to predict specific methane yields (SMY) with a dataset of 14 features from lignocellulosic biomass (LB) characteristics and operating conditions of completely mixed reactors under continuous feeding mode. The random forest (RF) model was best suited for predicting SMY with a coefficient of determination (R²) of 0.85 and root mean square error (RMSE) of 0.06. Biomass compositions greatly influenced SMYs from LB, and cellulose prevailed over lignin and biomass ratio as the most important feature. Impact of LB to manure ratio was assessed to optimize biogas production with the RF model. Under typical organic loading rates (OLR), optimum LB to manure ratio of 1:1 was identified. Experimental results confirmed influential factors revealed by the RF model and provided the highest SMY of 79.2% of the predicted value. Successful applications of ML for anaerobic digestion modelling and optimization specifically for LB were revealed in this work.

Thermocatalytic conversion of wood-plastic composite over HZSM-5 catalysts

Air gasification of the Wood-Plastic Composite (WPC) was performed over Ni-loaded HZSM-5 catalysts to generate H₂-rich gas. Increasing SiO₂/Al₂O₃ ratio (SAR) of HZSM-5 adversely affected catalytic activity, where the highest gas yield (51.38 wt%) and H₂ selectivity (27.01 vol%) were acquired using 20 %Ni/HZSM-5(30) than those produced over 20 %Ni/HZSM-5(80) and 20 %Ni/HZSM-5(280). Reducing SAR was also favorably conducive to increasing the acyclic at the expense of cyclic compounds in oil products. These phenomena are attributed to enhanced acid strength and Ni dispersion of 20 %Ni/HZSM-5(30) catalyst. Moreover, catalytic activity in the terms of gas yield and H₂ selectivity enhanced with growing Ni loading to 20 %. Also, the addition of promoters (Cu and Ca) to 20 %Ni/HZSM-5(30) boosted the catalytic efficiency for H₂-rich gas generation. Raising temperature indicated a positive relevance with the gas yield and H₂ selectivity. WPC valorization via gasification technology would be an outstanding outlook in the terms of a waste-to-energy platform.

Review on machine learning-based bioprocess optimization, monitoring, and control systems

Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bioprocessing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.

Artificial intelligence and machine learning-based monitoring and design of biological wastewater treatment systems

Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.

The Empowerment of Artificial Intelligence in Post-Digital Organizations: Exploring Human Interactions with Supervisory AI

Technology evolves together with humans. Across industrial revolutions, its role has evolved from that of a simple tool used by humans to that of intelligent decision-maker and teammate. In the post-digital era where ongoing advances in artificial intelligence are widely visible, the question arises regarding the extent to which technology will be “upgraded” into roles previously filled by human supervisors, thereby replacing persons in managerial positions. This text aims to delineate how the organizational role of technology has been transformed across decades and the forms that it currently takes within companies, with an eye to the future. We draw on posthuman managerial literature and known cases of organizations where some forms of supervisory artificial intelligence are already used. The text is conceptual-reflective by nature; it seeks to initiate a discussion on the many challenges that humanity will face in connection with the deployment of empowered posthuman agents in companies.

Integrating speed breeding with artificial intelligence for developing climate-smart crops

INTRODUCTION

In climate change, breeding crop plants with improved productivity, sustainability, and adaptability has become a daunting challenge to ensure global food security for the ever-growing global population. Correspondingly, climate-smart crops are also the need to regulate biomass production, which is imperative for the maintenance of ecosystem services worldwide. Since conventional breeding technologies for crop improvement are limited, time-consuming, and involve laborious selection processes to foster new and improved crop varieties. An urgent need is to accelerate the plant breeding cycle using artificial intelligence (AI) to depict plant responses to environmental perturbations in real-time.

MATERIALS AND METHODS

The review is a collection of authorized information from various sources such as journals, books, book chapters, technical bulletins, conference papers, and verified online contents.

CONCLUSIONS

Speed breeding has emerged as an essential strategy for accelerating the breeding cycles of crop plants by growing them under artificial light and temperature conditions. Furthermore, speed breeding can also integrate marker-assisted selection and cutting-edged gene-editing tools for early selection and manipulation of essential crops with superior agronomic traits. Scientists have recently applied next-generation AI to delve deeper into the complex biological and molecular mechanisms that govern plant functions under environmental cues. In addition, AIs can integrate, assimilate, and analyze complex OMICS data sets, an essential prerequisite for successful speed breeding protocol implementation to breed crop plants with superior yield and adaptability.

The influence of a two-step leaching pretreatment on the steam gasification properties of cornstalk waste

Knowing the effect of specific alkali and alkali earth metals forms is vital for the high-efficient gasification of biomass. This work developed a two-step leaching method to pretreat cornstalk, dividing the inorganic metals into water-soluble (K⁺, 74 wt%), acid-soluble (Al³⁺, Ca²⁺, Fe²⁺, etc) and insoluble (Si⁴⁺) substances. The water-soluble K⁺ was mainly in KCl form, the acid-soluble metals were removed in phosphates and sulfates forms. The rapid gasification properties of raw material, water leaching residue and acid leaching residue indicated that KCl was the key factor to enhance the hydrogen yield and gasification efficiency. Apart from K⁺, the alkali earth metals (Ca²⁺, Mg²⁺) also had a little catalytic effect on producing hydrogen. When the feedstock was out of metal cations, the syngas was mainly composed of CO. The basic ions to acid ions ratio was linearly related to the syngas quality, which could conduct the flux additives.

Co-Fermentation of Microalgae Biomass and Miscanthus × giganteus Silage—Assessment of the Substrate, Biogas Production and Digestate Characteristics

The development of a sustainable bioenergy market is currently largely fueled by energy crops, whose ever-increasing production competes with the global food and feed supply. Consequently, non-food crops need to be considered as alternatives for energy biomass production. Such alternatives include microalgal biomass, as well as energy crops grown on non-agricultural land. The aim of the present study was to evaluate how co-digestion of microalgal biomass with giant miscanthus silage affects feedstock properties, the biogas production process, biogas yields, methane fractions and the digestate profile. Combining giant miscanthus silage with microbial biomass was found to produce better C/N ratios than using either substrate alone. The highest biogas and methane production rates—628.00 ± 20.05 cm3/gVS and 3045.56 ± 274.06 cm3 CH4/d—were obtained with 40% microalgae in the feedstock. In all variants, the bulk of the microbial community consisted of bacteria (EUB338) and archaea (ARC915).

Sustainable valorization of algae biomass via thermochemical processing route: An overview

Biofuels have become an attractive energy source because of the growing energy demand and environmental issues faced by fossil fuel consumption. Algal biomass, particularly microalgae, has excellent potential as feedstock to be converted to bio-oil, biochar, and combustible syngas via thermochemical conversion processes. Third-generation biofuels from microalgal feedstock are the promising option, followed by the first-generation and second-generation biofuels. This paper provides a review of the applications of thermochemical conversion techniques for biofuel production from algal biomass, comprising pyrolysis, gasification, liquefaction, and combustion processes. The progress in the thermochemical conversion of algal biomass is summarized, emphasizing the application of pyrolysis for its benefits over other processes. The review also encompasses the challenges and perspectives associated with the valorization of microalgae to biofuels ascertaining the potential opportunities and possibilities of extending the research into this area.

Co-Hydrothermal Liquefaction of algal and lignocellulosic biomass: Status and perspectives

Hydrothermal liquefaction (HTL) effectively converts biomass to biofuels, thereby limiting the endless reliance on petroleum products derived from fossil fuels. However, the conversion is based on individual feedstock in the HTL process. In order to, further boost the conversion, HTL can be done by blending various feedstock, mainly algal and lignocellulosic biomass. Bibliometric analysis was carried out, and it was observed that there have been very few studies on Co-Hydrothermal Liquefaction (Co-HTL). There still exist several crucial gaps in process optimization when co-reactants are used due to their synergistic effects. The reaction kinetics and mechanism, catalyst screening and by-products application require further studies. Therefore, R&D is necessary to optimize the process to completely utilize the complementarity of the feedstocks under study resulting in better quality of products which require minor/ no upgradation steps.

Availability and Environmental Performance of Wood for a Second-Generation Biorefinery

The current global climate change, the 2030 Agenda, and the planetary boundaries have driven new development strategies, such as the circular economy, bioeconomy, and biorefineries. In this framework, this study analyzes the potential availability and sustainability of the wood supply chain for a small-scale biorefinery aiming at producing 280–300 L of bioethanol per ton of dry biomass, consuming 30,000 t of dry biomass per year harvested in a 50 km radius. This wood production goal was assessed from Eucalyptus grandis stands planted for solid wood in northeastern Uruguay. Moreover, to understand the environmental performance of this biomass supply chain, the energy return on investment (EROI), carbon footprint (CF), and potential soil erosion were also assessed. The results showed that the potential wood production would supply an average of 81,800 t of dry mass per year, maintaining the soil erosion below the upper threshold recommended, an EROI of 2.3, and annual CF of 1.22 kg CO2−eq m−3 (2.6 g CO2−eq MJ−1). Combined with the environmental performance of the bioethanol biorefinery facility, these results would show acceptable values of sustainability according to EU Directive 2009/28/ec because the bioethanol CF becomes 1.7% of this petrol’s CF.