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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.

Achieving the goals of sustainable development and poverty reduction implies an important condition for access to electricity for the entire population. In the economic literature, the relationship between electricity consumption and economic growth has different perspectives. The lack of good governance within an economy, besides the deficiencies of energy resources, is a key issue in worsening energy issues for developing countries. These countries have failed to alleviate the energy crises that have hindered development prospects, amid flourishing corruption and inefficient governments. Our research, using a panel methodology, analyzes the long-term relationship between energy consumption, economic growth and good governance for 14 Central and Eastern European countries, over the period 1995–2017. The study demonstrates empirically that there is a causal relationship between electricity consumption and economic growth, underlining the fact that deficiencies in the energy system lead to slowing economic growth. The study also shows that good governance influences electricity and Gross Domestic Product (GDP) consumption, and the governments from Central and Eastern European countries have to restore good governance in the economy, creating an environment conducive to investment in the energy sector, which would increase competition and reduce inefficiencies in the production, transmission, and distribution of energy.

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.

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