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Abstract: The significance of the agricultural sector in the economy of Republika Srpska, as well as the importance of dairy cattle farming within agriculture, determined the thematic field of the research, which had several key research objectives. The first objective of the dissertation was to determine the economic efficiency of the investment in milk production and sales on family farms with 8-15 dairy cows in Republika Srpska. The second objective was to assess the risk, or volatility, of such investment projects. The third objective was to calculate the additional value of diversification in transitioning from selling milk to milk processing into cheese on the same farm and determine the additional project's strategic value. The final objective was to compare the case studies of the analyzed farms based on selected indicators. Primary data were collected by interviewing five farmers who produce milk and process it into cheese while secondary data were obtained from various sources. Key data processing methods included Cost-Benefit Analysis, Monte Carlo simulations, and Black-Scholes and Binomial methods for evaluating real options. Based on the results of the analysis of milk production projects on small farms in Republika Srpska, it was concluded that these projects are economically unsustainable due to their negative and insufficiently satisfactory indicators. Through Monte Carlo simulations, it was determined that changes in input and output prices have an impact on such projects. Diversification of milk production into cheese processing on small farms is economically efficient, as the option values of the additional project are satisfactory and the NPVS, representing the total value of the additional project, ranges from €28,000 to €143,000 for all five analyzed farms. Based on the farm analysis, the conclusion is that the farm with the highest number of dairy cows implements an extensive feeding method, and sells its products at a prominent tourist location in rural areas has the best indicators and represents the best business model for small milk farms in the territory of Republika Srpska.
Keywords: Agriculture, Milk farming, Cheese production, Cost-Benefit Analysis, Real Options Approach, Monte Carlo Simulation
Published in DKUM: 13.03.2025; Views: 0; Downloads: 1
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Abstract: Methanol is a versatile substance that can be used in combustion engines and fuel cells and as a feedstock for the production of various chemicals. However, the majority of methanol is currently produced from fossil fuels, which is not sustainable. The aim of this study was to analyze and evaluate the feasibility of methanol production from renewable sources as a bridge to a low-carbon economy and its potential as an alternative to fossil-derived chemicals. For this purpose, the process of methanol production from captured CO2 and water as an H2 source was simulated in Aspen Plus. For CO2 capture, the monoethanolamine (MEA) absorption process was assumed. The H2 required for methanol synthesis was obtained by alkaline water electrolysis using electricity from renewable sources. The captured CO2 and the produced H2 were then converted into methanol through the process of CO2 hydrogenation in two ways, direct and two-step synthesis. In the direct conversion, the hydrogenation of CO2 to methanol was carried out in a single step. In the two-step conversion, the CO2 was first partly converted to CO by the reverse water-gas shift (RWGS) reaction, and then the mixture of CO and CO2 was hydrogenated to methanol. The results show that direct synthesis has a higher methanol yield (0.331 kmol of methanol/kmol of H2 ) compared to two-step synthesis (0.326 kmol of methanol/kmol of H2 ). The direct synthesis produces 13.4 kmol of methanol/MW, while the two-step synthesis produces 11.2 kmol of methanol/MW. This difference amounts to 2.2 kmol of methanol/MW, which corresponds to a saving of 0.127 $/kmol of methanol. Besides the lesser energy requirements, the direct synthesis process also produces lower carbon emissions (22,728 kg/h) as compared to the two-step synthesis process (33,367 kg/h).
Keywords: power-to-X, Aspen Plus, methanol, CO2 capture, methanol production, water electrolysis
Published in DKUM: 12.02.2025; Views: 0; Downloads: 5
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Abstract: Anaerobic digestion (AD) is a promising and yet a complex waste-to-energy technology. To optimize such a process, precise modeling is essential. Developing complex, mechanistically inspired AD models can result in an overwhelming number of parameters that require calibration. This study presents a novel approach that considers the role of trace metals (Ca, K, Mg, Na, Co, Cr, Cu, Fe, Ni, Pb, and Zn) in the modeling, numerical simulation, and optimization of the AD process in a batch bioreactor. In this context, BioModel is enhanced by incorporating the influence of metal activities on chemical, biochemical, and physicochemical processes. Trace metal-related parameters are also included in the calibration of all model parameters. The model’s reliability is rigorously validated by comparing simulation results with experimental data. The study reveals that perturbations of 5% in model parameter values significantly increase the discrepancy between simulated and experimental results up to threefold. Additionally, the study highlights how precise optimization of metal additives can enhance both the quantity and quality of biogas production. The optimal concentrations of trace metals increased biogas and CH4 production by 5.4% and 13.5%, respectively, while H2, H2S, and NH3 decreased by 28.2%, 43.6%, and 42.5%, respectively.
Keywords: anaerobic digestion, batch bioreactor, methane production, model parameters calibration, active set optimization method, perturbation of model parameter, gradient based optimization, trace metals
Published in DKUM: 30.01.2025; Views: 0; Downloads: 3
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Abstract: Every year approximately 70 million passenger cars are being produced and automotive industry is much bigger then just passenger cars. The impact of automotive industry on the environment is tre-mendous. From extracting raw materials through manufacturing and assembly processes, exploitation of the vehicle to the reprocessing irreversible, extensive environmental damage is done. The goal of this study is to show how implementing eco-design processes into supply chain management can re-duce the impact of automotive industry on the environment by e.g. reducing the use of the fuel, in-creasing the use of recycled materials. Focus is on evaluation of current state, environmental impacts and potential improvements for design, raw materials, manufacturing and distribution and end-of-life phase.
Keywords: eco-design processes, automotive industry, supply chain management, business processes, cleaner production, environmental impacts, logistics
Published in DKUM: 18.12.2024; Views: 0; Downloads: 4
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Abstract: Artificial intelligence (AI) has found applications in enterprises′ production planning processes. However, a critical question remains: could AI replace human planners? We conducted a comparative analysis to evaluate the main task of planners in an intermittent process: planning the duration of production orders. Specifically, we analysed the results of a human planner using master data and those of an AI algorithm compared to the actual realisation. The case study was conducted in a large production company using a sample of production products and machines. We were able to confirm two of the three research questions (RQ1 and RQ3), while the results of the third question (RQ2) did not meet our expectations. The AI algorithms demonstrated significant improvement with each iteration. Despite this progress, it is still difficult to determine the exact threshold at which AI outperforms human planners due to the unpredictability of unexpected events. Even though AI significantly improves prediction accuracy, the inherent variability and incomplete input data pose a major challenge. As progress is made, robust data collection and management strategies need to be integrated to bridge the gap between the potential of AI and its practical application, fostering the symbiosis between human expertise and AI capabilities in production planning.
Keywords: artificial intelligence, machine learning, production processes, production planning, production scheduling
Published in DKUM: 04.12.2024; Views: 0; Downloads: 18
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