Iskanje po katalogu digitalne knjižnice

Opis: Waste heat recovery technologies play an important role in enhancing energy efficiency and supporting sustainable energy production. This study investigates the utilization of waste heat from aluminium production through an Organic Rankine Cycle (ORC) system to generate electricity and heat simultaneously. Based on operational data from an aluminium plant, the system is firstly optimized from both the thermodynamic and economic perspectives. To maximize performance and to identify optimal configurations, a mathematical model is developed and solved using GAMS, capturing the complex interdependencies between the operational, economic and thermodynamic parameters. The environmental impact of the optimized scenarios is subsequently evaluated using a Life Cycle Assessment (LCA), considering a broad range of impact categories. The results indicate a maximum power output of 830.9 kW and a maximum net present value (NPV) of 51.71 M€, confirming the system’s technical and economic viability. The environmental assessment demonstrates the potential of ORC systems as sustainable energy solutions, with significant environmental unburdening under optimized operating conditions (up to -606.0 kg CO2 eq./h). A sensitivity analysis indicates that the greatest environmental benefits occur under the optimal thermodynamic scenario, achieved through the utilization of higher-energy flue gas streams (up to -515.0 kg CO2 eq./h), and under the optimal economic scenario by balancing the electricity and heat prices optimally for simultaneous heat and power production (up to -696.7 kg CO2 eq./h). These findings highlight the importance of the thermal input quality and availability in maximizing ORC performance. With the ability to prioritize electricity, heat, or both, the optimized ORC systems support flexible energy solutions tailored to specific applications and environmental conditions, offering a promising pathway for unburdening the environment through the efficient utilization of industrial waste heat.
Ključne besede: waste heat recovery, aluminium production, organic rankine cycle, environmental impact, life cycle assessment, sustainable energy solutions
Objavljeno v DKUM: 13.06.2025; Ogledov: 0; Prenosov: 16
Celotno besedilo (1,30 MB)

Opis: This study presents an integrated design and optimization of an Organic Rankine Cycle (ORC) for the recovery of waste heat from aluminium production. Non-Linear Programming (NLP) models were developed, with the objectives of maximizing electricity production and the Net Present Value (NPV) of the system. The models account for optimizing the operating conditions and changes in thermodynamic features of the system. The developed models are applied to a case study of Slovenian aluminium company where the performance of three different working fluids (R245fa, R1234yf and R1234ze) are compared. The optimization is performed considering different temperatures and prices of produced hot water and electricity, minimum approach temperature (DTmin), concentration of CO2 in flue gas and temperature and flowrate of flue gas. Results show that the selected working fluids for the proposed waste heat-based ORC system have the potential to substitute up to about 830 kW of electricity in a sustainable and economic manner. Out of the three working fluids considered, R245fa showed up to 7.9% efficiency of the ORC cycle and was identified as the best performing working fluid considering both economic viability and the amount of electricity produced by the system, however the refrigerant inherently has higher GHG footprint.
Ključne besede: waste heat, waste heat utilization, aluminium industry, organic rankine cycle, power generation, optimization
Objavljeno v DKUM: 10.04.2025; Ogledov: 0; Prenosov: 7
Celotno besedilo (2,73 MB)
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Ključne besede: multiple heat sources, Organic Rankine cycle, separate and cascade designs, Steam Rankine cycle, thermodynamic optimisation
Objavljeno v DKUM: 28.02.2025; Ogledov: 0; Prenosov: 3
Celotno besedilo (5,16 MB)

Opis: Utilization of biomass through anaerobic digestion and hydrothermal carbonization is crucial to maximize resource efficiency. At the same time, supply chain integration ensures sustainable feedstock management and minimizes environmental and logistical impacts, enabling a holistic approach to a circular bioeconomy. This study presents an integrated approach to simultaneously optimize the biomass supply chain network and process flowsheet, which includes anaerobic digestion, cogeneration, and hydrothermal carbonization. A three-layer supply chain network superstructure was hence developed to integrate the optimization of process variables with supply chain features such as transportation modes, feedstock supply, plant location, and demand location. A mixed-integer nonlinear programming model aimed at maximizing the economic performance of the system was formulated and applied to a case study of selected regions in Slovenia. The results show a great potential for the utilization of organic biomass with an annual after tax profit of 23.13 million USD per year, with the production of 245.70 GWh/yr of electricity, 298.83 GWh/yr of heat, and 185.08 kt/yr of hydrochar. The optimal configuration of the supply chain network, including the selection of supply zones, plant locations and demand locations, transportation links, and mode of transportation is presented, along with the optimal process variables within the plant.
Ključne besede: anaerobic digestion, hydrothermal carbonization, supply chain optimization, process flowsheet optimization, mathematical programming
Objavljeno v DKUM: 07.02.2025; Ogledov: 0; Prenosov: 11
Celotno besedilo (2,71 MB)

Opis: Excessive production, indiscriminate consumption, and improper disposal of plastics have led to plastic pollution and its hazardous environmental effects. Various approaches to tackle the challenges of reducing the plastic footprint have been developed and applied, such as the production of alternative materials (design for recycling), the production and use of biodegradable plastic and plastics from power-to-X, and the development of recycling approaches. This study proposes an optimisation strategy based on regression to evaluate and predict plastic use and end-of-life fate in the future based on historical trends. The mathematical model is formulated and correlations based on functions of time are developed and optimised by minimising the sum of squared residuals. The plastic quantities up to the year 2050 are projected based on historical trends analysis, and for improved sustainability, projections are additionally based on intervention analyses. The results show that the global use of plastics is expected to increase from 464 Mt in 2020 up to 884 Mt in 2050, with up to 4725 Mt of plastics accumulated in stock in 2050 (from the year 2000). Compared to other available forecasts, a slightly lower level of plastic use and stock are obtained. The intervention analysis estimates a range of global plastics' consumption between 594 Mt and 1018 Mt in 2050 by taking into account its different increment rates (between −1 % and 2.65 %). In the packaging sector, the implementation of reduction targets (15 % reduction in 2040 compared to 2018) could lead to a 27.3 % decrease in plastic use in 2050 as compared to 2018, while achieving recycling targets (55 % in 2030) would recycle >75 % of plastic packaging in 2050. The partial substitution of fossil-based plastics with bioplastics (polyethylene) will require significant land area, between 0.2 × 106 km2 for obtaining switchgrass and up to around 1.0 × 106 km2 for obtaining forest residue (annual yields of 58.15 t/ha and 3.5 t/ha) in 2050. The intervention analysis shows that proactive policies can mitigate sustainability challenges, however achieving broader sustainability goals also requires reduction of footprints related to energy production and virgin plastic production, the production of bio-based plastics, and the full implementation of recycling initiatives.
Ključne besede: plastic use, plastic waste, end-of-life fate, forecasting, hostorical trends, regression analysis, least square method, intervention analysis
Objavljeno v DKUM: 31.01.2025; Ogledov: 0; Prenosov: 10
Celotno besedilo (1,08 MB)
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