The pretreatment of lignocellulosic biomass (LC biomass) prior to the anaerobic digestion (AD) process is a mandatory step to improve feedstock biodegradability and biogas production. An important potential is provided by lignocellulosic materials since lignocellulose represents a major source for biogas production, thus contributing to the environmental sustainability. The main limitation of LC biomass for use is its resistant structure. Lately, biological pretreatment (BP) gained popularity because they are eco-friendly methods that do not require chemical or energy input. A large number of bacteria and fungi possess great ability to convert high molecular weight compounds from the substrate into lower mass compounds due to the synthesis of microbial extracellular enzymes. Microbial strains isolated from various sources are used singly or in combination to break down the recalcitrant polymeric structures and thus increase biogasgeneration. Enzymatic treatment of LC biomass depends mainly on enzymes like hemicellulases and cellulases generated by microorganisms. The articles main purpose is to provide an overview regarding the enzymatic/biological pretreatment as one of the most potent techniques for enhancing biogas production.

In order to obtain bioenergy (biogas, biofuel) or pellets, different types of lignocellulosic biomass are subjected to a mechanical pretreatment, first by size reduction, then by separating, and ultimately by fracturing or bio-refining. Biomass processing mainly refers to a grinding process that occurs until reaching certain limits. The size reduction process, such as grinding, is an operation that is executed with different levels of energy consumption, considering biomass mechanical characteristics and the necessary grinding level. This paper, illustrates a comparative analysis of experimental results obtained by grinding multiple types of vegetal biomass (Miscanthus, corn stalks, alfalfa, willow) used in the process of bio-refining and bio-fracturing. Experiments were realized using both a laboratory knife mill Grindomix GM200 (Retsch GmbH, Haan, Germany), and a 22 kW articulated hammer mill, using different grinding system speeds and different hammer mill sieves. Results have shown that biomass mechanical pre-processing grinding leads to supplementary costs in the overall process through bio-refining or bio-fracturing in order to obtain bio-products or bio-energy. So, specific energy consumption for grinding using a hammer mill can reach 50–65 kJ/kg for harvested Miscanthus biomass, and 35–50 kJ/kg for dried energetic willow, using a 10 mm orifice sieve, values which increase processing costs.

In the context of increasing pressure regarding the sustainable utilization of food waste in a circular economy, one of the trends is their biological transformation, through anaerobic digestion, into biogas as a renewable source of energy. We presented the physical-chemical properties of the main categories of food waste from different sources: dairy, meat, and poultry, fish, fruit and vegetable, cereal and bakery, brewing and winery industries, and others. Due to the high organic load, the presence of a multitude of nutrients, and an insignificant amount of inhibitors, food waste can be successfully used in the biogas production process in co-digestion with other materials. Physical (mechanical and thermal), chemical (alkali, acid, and oxidative), and biological (enzymatic, bacterial, and fungal) techniques have been widely used for pretreatment of different substrate types, including food waste. These pretreatments facilitate the degradation of pretreated food waste during anaerobic digestion and thus lead to an enhancement in biogas production. The purpose of this study is to review the situation of food waste generated in the food industry and to formulate the main trends of progress in the use of this waste in the anaerobic digestion process.

Technology is being used in our society in all areas, mostly in industry, and generates the most interest in current research since it is a part of day-to-day activities. The main objective of this research was to use bibliometric analysis to analyze the production of scientific literature on digital twin and smart manufacturing with a focus on Industry 4.0, using information from the Web of Science database. To conduct the study, the keywords necessary for data selection were chosen, and then analyzed based on different variables such as author productivity, citations, most productive institutions, publishers with the highest number of publications, scientific document classification, countries with the highest number of publications, and a network analysis using VOSviewer. The results showed Tao F. and Soderberg R. were the main authors, that China was the country with the highest knowledge, and Elsevier was the main publisher. Although the subject has only been in publication for five years, digital twin will constitute an important part of future technologies due to its rapid ascension, proof of this being its yearly productivity (2020 producing the highest number of materials). Papers published in 2021 were excluded, but the difference between the numbers of materials found and those analyzed shows that 2021 will be even more productive than 2020.

In this paper, the latest research in the field of anaerobic co-digestion related to the advantages of using different mixtures of substrates on the performance of the process and increasing its efficiency is reviewed. The main aspects presented in this review refer to the study of the most commonly used types of substrates, highlighting their characteristics, the diversity of microbial communities involved in the production of biogas, the applied pretreatments, and the possibility of obtaining an improved digestate as a secondary product. The main types of substrates used in anaerobic co-digestion are food waste, sewage sludge, animal manure, lignocellulosic biomass, algae, fats, oils, and greases. The data from the studied works demonstrated that the anaerobic co-digestion process improves the carbon/nitrogen ratio and nutrient balance, increases the process stability, and diminishes the concentration of toxic inhibitors. At the same time, the use of appropriate mixtures of substrates leads to an increase in the diversity of microbial communities, among which synergistic relationships are established that ultimately favor the growth of the methanogenic potential. Finally, based on the research results found, one of the main trends is the need to adapt technology to the type of substrate and the industry.

Climate awareness caused by the threat of global warming is the number one agenda item for developed and developing economies. Plans developed in this context, environmentally friendly trends in economic activities, and countries’ efforts to adapt to sustainable development have enabled new road maps. The most important of these efforts is the Paris Climate Agreement signed in 2015 and the Green Deal implemented by the European Union (EU) within the framework of this agreement. In this study, the carbon emissions of Turkey, which has important trade relations with the EU, were estimated using machine learning techniques, and a prediction was made for 2030 based on the results obtained. These results were evaluated in line with the targets of the Green Deal. The R² of support vector regression (SVR), which was applied in the model as one of the machine learning techniques, was found to be 98.4%, and it was found to have the highest predictive power. This technique was followed by multiple linear regression (MLR) with a 97.6% success rate and artificial neural networks (ANN) with a 95.8% success rate, respectively. According to the estimates achieved with the most successful model, SVR, Turkey’s carbon emissions are expected to be 723.97 million metric tons (mt) of carbon dioxide (CO₂) in 2030, the target year set by the EU. This level is 42% higher than the target that needs to be achieved given the data existing in 2019. According to these results, Turkey will not be able to reach the targets set by the EU unless it increases its coal-based energy consumption and provides incentives for renewable energy sources.