This article presents the opportunities for constructing a global data base picturing underlying trends that drive global climate change. Energy-related CO2 emissions currently represent the key impact on climate change and thus become here the object of deep, long-term and historiographic analysis. In order to embrace all involved domains of technology, energy economy, fuel shares, economic efficacity, economic structure and population, a “Global Change Data Base” (GCDB) is suggested, based on earlier worldwide accepted data repositories. Such a GCDB works through regressions and statistical analysis of time series of data (on extensive magnitudes such as energy demand, population or Gross Domestic Product, GDP) as well as generation of derived data such as quotients of the former, yielding intensive magnitudes that describe systems and their structural properties. Moreover, the GCDB sets out to compute the first and second time derivatives of said magnitudes (and their percentual shares) which indicate new long-term developments already at very early phases. The invitation to participate in this foresight endeavour is extended to all readers. First preliminary GCDB results quantitatively portray the evolutionary structural global dynamics of economic growth, sectoral economic shifts, the shifts within energy carriers in various economic sectors, the ongoing improvements of energy intensity and energy efficiency in many economic sectors, and the structural changes within agricultural production and consumption systems.
Numerical simulation is a widely used tool for studying CO2 storage in porous media. It enables the representation of trapping mechanisms and CO2 retention capacity. The complexity of the involved physicochemical phenomena necessitates multiphase flow, accurate fluid and rock property representation, and their interactions. These include CO2 solubility, diffusion, relative permeabilities, capillary pressure hysteresis, and mineralization, all crucial in CO2 trapping during carbon storage simulations. Experimental data is essential to ensure accurate quantification. However, due to the extensive data required, modeling under uncertainty is often needed to assess parameter impacts on CO2 trapping and its interaction with geological properties like porosity and permeability. This work proposes a framework combining laboratory data and stochastic parameter distribution to map uncertainty in CO2 retention over time. Published data representing solubility, residual trapping, and mineral trapping are used to calibrate prediction models. Geological property variations, like porosity and permeability, are coupled to quantify uncertainty. Results from a saline sandstone aquifer model demonstrate significant variation in CO2 trapping, ranging from 17% (P10 estimate) to 56% (P90), emphasizing the importance of considering uncertainty in CO2 storage projects. Quadratic response surfaces and Monte Carlo simulations accurately capture this uncertainty, resulting in calibrated models with an R-squared coefficient above 80%. In summary, this work provides a practical and comprehensive framework for studying CO2 retention in porous media, addressing uncertainty through stochastic parameter distributions, and highlighting its importance in CO2 storage projects.
Besides the increase in global energy demand, access to clean energy, reduction in greenhouse gas emissions caused by conventional power generation techniques, energy security, and availability of electricity in remote villages in emerging nations are some of the factors that foster the use of renewable energy sources (RESs) in generating electricity. One of the aims of initiating microgrids (MGs) is to maximize the benefits of RES while alleviating grid-connect issues. Microgrids are interconnected RESs and electrical loads within clearly delineated electrical limits that operate as individual controllable units on the electrical network. It can operate independently and be grid-connected. The paper presents a review and performance assessment of renewable energy-based microgrids under various operating scenarios in stand-alone, grid-connected, and transitioning modes of operation. Fault occurrences, an increase in micro-source generation, a load increase, and the sudden disconnection of a micro-source are some of the simulated scenarios. Microgrid network components’ performance, such as the bidirectional DC-DC converter and energy storage system (ESS), was evaluated. The simulated microgrid architecture includes a small hydroelectric plant, wind farm, and ESS. The work provides valuable information to energy stakeholders on the performance of microgrids in low-voltage distribution networks. The microgrid is coupled to a low-voltage distribution network (0.415 kV) via a PCC. The system under investigation is modeled and simulated using MATLAB/Simulink. From the simulation analysis, the fault effect was felt on the utility and did not escalate to the microgrid side during stand-alone operation. Power quality issues, such as voltage rise, are some of the challenges identified during the transition from one mode of operation to another. However, the energy storage system responds to disturbances and maintains system stability. The originality of this paper is based on evaluating different modes of operation of microgrids and comparing system performances under various operating conditions.
Oil is an unsustainable energy since it is non-renewable. However, oil may not be completely replaced in a short time, so the environmental problems caused by the oil development still require our attention. The oily sludge is a kind of hazardous waste produced during the oil development. To reduce the environmental impact caused by oily sludge, low-carbon and sustainable treatment technologies need to be selected. The incineration, chemical extraction and thermal desorption are common technologies for treatment of oily sludge. We calculated the carbon emissions of these technologies. Then the index evaluation system of oily sludge treatment technology was established with the environmental, economic, social, and technical factors. And the weight of evaluation index was determined by the analytic hierarchy process (AHP). Through the investigation of industry experts, we evaluated the treatment technologies by the fuzzy comprehensive evaluation method (FCE). The results showed that the carbon emissions of incineration are 42.70 t CO2-eq/t which is the highest. Meanwhile, it is 4.80 t CO2-eq/t and 0.10 t CO2-eq/t for chemical extraction and thermal desorption, respectively. The comprehensive scores of incineration, chemical extraction and thermal desorption were 4.59, 5.16 and 4.95, respectively. Therefore, the chemical extraction technology is an optimal treatment technology for oily sludge with the relatively low carbon emission and the highest comprehensive technical score. At the same time, the thermal desorption technology has strong application potential with the lowest carbon emissions. This result provides a reference for achieving clean and sustainable energy development processes.
The transition to clean and sustainable energy sources is crucial for combating the challenges posed by climate change. Green hydrogen, produced through renewable energy-driven electrolysis, holds significant promise as a viable clean energy carrier. The study introduces a system that leverages abundant solar energy and utilizes seawater as the feedstock for electrolysis, potentially offering a cost-effective solution. A comprehensive mathematical model, implemented in MATLAB, is employed to simulate the design and operational efficiency of the proposed green hydrogen production system. The system’s core components include solar panels as a clean energy source, an advanced MPPT charge controller ensuring optimal power delivery to the electrolyzer, and a seawater tank serving as the electrolyte source. The model combines these elements, allowing for continuous operation and efficient hydrogen production, addressing concerns about energy losses and cost-effectiveness. Results demonstrate the influence of solar irradiance on the system’s performance, revealing the need to account for seasonal variations when designing green hydrogen production facilities. Theoretical experiments are conducted to evaluate the behavior of a lithium battery, essential for stabilizing the system’s output and ensuring continuous operation during periods of low solar radiation.
Nowadays, increasing attention is directed towards the sustainable use of raw materials. For a circular economy, recovery from spent devices represents a fundamental practice. With the transition to electric mobility, an increasing number of devices powered by lithium batteries are produced. Indeed, this is the fastest growing sector producing spent batteries, which are an important secondary source of critical raw materials, such as lithium, cobalt, graphite, and nickel. Therefore, this work aims to quantify the economic impact of recovering raw materials from lithium batteries used in the electric vehicles sector. Based on the chemical composition of the various lithium batteries and their market diffusion, the intrinsic economic value of this waste has been estimated to be around 6500 €/ton. Starting from the literature data on the global energy demand from lithium batteries and deriving the trend of their specific energy over time, the mass of material introduced into the market annually is estimated to reach 60 Mton/year by 2040. The annual amount of end-of-life lithium batteries was calculated by applying the Weibull distribution to describe the probability of failure, yielding 10 Mton/year by 2040. Finally, based on these results, the economic impact of the recovery market was assessed for two different scenarios.
This study explores the transient characteristics of a drain water heat recovery (DWHR) device employed for heat recovery from warm grey water in buildings. Experimental measurements were conducted to investigate the response time of the DWHR device under various flow conditions. The thermal performance of the system was assessed using both transient and steady-state effectiveness analyses. The findings reveal that the response time is influenced by the water volume within the system, with an increase observed, and by the water flow rate, which leads to a decrease in response time. Additionally, a decrease in effectiveness is noted when hot water is used in short and frequent intervals. Furthermore, an economic analysis demonstrates that considering the transient behavior of the device results in a significant overall decrease of 37% in annual savings. Specifically, the usage of sinks exhibits a reduction in annual savings by 56%, while showers show a decrease of 13% in annual savings.
Dairies which produce cheese and milk products can, however, produce large volumes of wastewater that require treatment, usually via activated sludge treatment. Disposal of the resulting activated sludge to land is viewed favorably as the sludge is rich in phosphorus (P) and nitrogen (N) and enables nutrient recycling. Nonetheless, sludge management can significantly influence the greenhouse gas (GHG) emissions to the atmosphere. This manuscript has modelled the GHG emissions arising from two sludge management strategies currently adopted by Danish dairies whereby: (i) sludge is stored and later applied to fields; or (ii) sludge is treated by anaerobic digestion (AD), stored, and the digestate will later be applied to fields. This is compared to (iii) an alternative sludge management strategy with treatment by Hydrothermal Carbonization (HTC). HTC is a technologically simple sludge treatment that could lower the cost for dewatering dairy sludge, forming a biochar-like material known as hydrochar. The produced hydrochar can be applied to the land for the purpose of carbon sequestration, P and N recycling. Our calculations indicate that GHG balances of HTC sludge management can result in a net carbon sequestration of 63 kg CO2eq per ton sludge, as opposed to net emissions of 420 and 156 kg CO2eq per ton sludge for strategies (i) and (ii), therefore offering significant reductions GHG emissions for the dairy sector.
Climate change is one of the most critical sustainability challenges facing the humanity. International communities have joined forces to mitigate climate change impact and aim to achieve carbon neutrality in the coming decades. To achieve this ambitious goal, life cycle thinking can play critical roles. Specifically, life cycle thinking helps evaluate the true climate impacts to avoid shifting emissions across processes in a product life cycle. It can also help inform consumers with carbon footprint information to make climate-conscious choices. Finally, it can help identify key processes dominating the carbon footprint of a product so that future improvement can set priorities. High quality data is required for accurate and timely carbon footprint accounting and critical challenges exist to obtain and share such data.
