Model matematyczny łańcucha dostaw surowców do produkcji biogazu

Rosnąca emisja gazów cieplarnianych oraz malejąca ilość paliw kopalnych powodują wzrost zainteresowania alternatywnymi źródłami energii. Biogaz jest niekonwencjonalnym paliwem wytwarzanym z materii organicznej w warunkach beztlenowych w procesie fermentacji metanowej. W pracy przedstawiono porównanie i analizę wykorzystywanych obecnie substratów w gospodarce biogazowej. W celu optymalizacji łańcucha dostaw surowców do produkcji biogazu opracowano model matematyczny w oparciu o schemat ideowy przepływu surowców i produktów w łańcuchu wartości produkcji biogazu. W obliczeniach wykorzystano procedury Mixed-Integer Linear Programing (MILP) programu MATLAB.

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The increasing energy demands together with flue gas emissions resulting from conventional energy sources accelerates the research for renewable energy and technologies such as anaerobic digestion (AD) to limit the environmental damage [1]. Anaerobic digestion process depends on a four biological steps (hydrolysis, acidogenesis, acetogenesis, and methanogenesis). involving different microbial species such as bacteria and archaea [4]. The stability of the AD process as well as the biogas yields depends on the characteristics of the available feedstocks, C/N ratio, biodegradability, nutrient content or buffering capacity. Generally this process depends on several relevant parameters: feedstock type and its composition, organic loading rate, fermentation temperature, pH, hydraulic retention time and carbon to nitrogen ratio. Methane yields and process stability can be impacted by different shortcomings such as low biodegradation, lag-phase, foam formation, over-acidification and high apparent viscosity or inhibitory elements. In this work the main feedstocks were compared and analyzed. The analysis shows that carbon-nitrogen ratio (C/N) is the most important factor to produce a biomethane. The (C/N) ratio is also important in the quantity of biogas production, even low deviances may cause pH changes to either volatile fatty acid or dangerous ammonia accumulation. It was found that systems containing less than 50% manure show different pH correlations and reduced C/N ratio. Any changes in pH may cause inhibition in biogas production as microbial performance reduces. The low C/N ratio of 15–25 is vital for good performance, whilst especially manure-heavy digestions perform better under higher pH conditions. In order to formulate a mathematical model optimizing the biogas production value chain, it is necessary to know the process parameters given above, as well as the costs of substrates and their transport, the distance between the place of obtaining substrates and the biogas plant, investment and operating costs of the biogas plant, and profits from the sale of biogas/biomethane. Additionally, in the future, the cost of purifying biogas into biomethane should be taken into account when determining the profitability of biomethane production. Subsidies and subsidies supporting the production of zero-emission fuels should also be taken into account.. The flow diagram of raw materials xi,j,k and products yk,n in the proposed model is shown in Figure 1. On its basis, a mathematical model was defined that takes into account the economic benefits of energy and biogas production, which takes into account the costs of biogas production, the costs of transporting raw materials, the possible location of plants where the biogas production is carried out. is the biogas production process. In the case of electricity generation from biogas, the proposed model can take into account the possible installation of a power plant. Operation costs (KOpx) and return on investment costs (KCap) were estimated on the basis of data included in [22]. In calculations based on the mathematical model the MixedInteger Linear Programming (MILP) procedures of MATLAB were used. MILP is a fast procedure for calculating optimal values, minima and maxima for complex mathematical models. For this purpose, the model equations were transformed to the form required by MATLAB [28].