Minimize CO2 Emissions Research Articles

Durability and hardened characteristics of cement mortar incorporating waste plastic and Polypropylene exposed to MgSO4 attack

Annual waste plastic disposal has grown, harming nature. Utilising this waste in concrete production may help preserve building resources. This study tested cement mortar with polyvinyl chloride (PVC) and polypropylene (PP) substituted for sand aggregate at 0, 5, 10, 15, and 20%. The samples were nevertheless exposed to a 10% and 20% MgSO4 solution for a month. The properties of both fresh and hardened materials under these circumstances have been evaluated and contrasted with those evaluated under typical circumstances. For mixes including PP and PVC, the flow diameter increased. The rounded plastic particles provided fewer contact surfaces and less friction among mixtures, which reduced water consumption and improved workability, leading to an increase in slump flow. As the amount of plastic aggregate increases, the compressive strength decreased. Moreover, this pattern might be explained by the weakening of the bond between the surfaces of the plastic aggregate and cement paste. The hydration of cement may also be hampered by the hydrophobic properties of plastic aggregate. Like compressive strength, the splitting tensile strength decreased as the replacement level of plastic ratio increased regardless of its type under all conditions (normal and exposing to MgSO4). PP and PVC fine aggregate in mortar increases sorptivity under all situations. Following screening, those circumstances and PVC have the most impact on compressive strength. increasing PVC and PP at 10% for each of them leads to lower values of compressive and tensile strength. An optimization process was implemented to determine the optimum value of PVC, PP, and MgSO4. It shows that using PVC of 3.9%, PP of 10.1%, and MgSO4 of 19.59% leads to maximum compressive and tensile strength with the minimum cost and CO2 emissions.

A Simheuristic Approach to Scheduling Sustainable and Reliable Maintenance for Bridge Infrastructure

Designing maintenance strategies for a vast portfolio of aging infrastructures requires decision-makers to ensure adequate safety levels while addressing the requirements on service interruptions, costs, and workforce availability. This study addresses the problem of scheduling maintenance interventions for a portfolio of bridges, aiming to minimize CO2 emissions while meeting minimum reliability requirements and adhering to workforce and budget constraints. To achieve this, we present a Simheuristic algorithm that combines a metaheuristic core based on the Adaptive Large Neighborhood Search metaheuristic with a Monte Carlo simulation module. This integration allows for the evaluation of optimized scheduling solutions, accounting for the inherent randomness in the structural deterioration process. The proposed approach is tested in a comparative analysis against traditional time-based and condition-based scheduling methods. Results from diverse bridge portfolios demonstrate that the proposed algorithm offers improved performance in terms of both total costs and CO2 emissions.

An innovative quick-decision strategy for evaluating the applicability of extractive distillation process with a preconcentration column in separating high-concentration organic waste liquid

In the face of the high energy consumption challenge in separating high-concentration organic waste liquid through extractive distillation (ED), the concept of preconcentration provides a new strategic direction. Therefore, it has become crucial to determine the suitability of the ED process with a preconcentration column for processing such complex mixture. Taking this into consideration, the study proposes an innovative quick-decision method that utilizes thermodynamic analysis of ternary phase diagram to determine the ratio of theoretical product flowrate at the bottom of the preconcentration column to the feed flowrate, which serves as the basis for deciding whether to choose the ED process with a preconcentration column for handling such separation tasks. Three azeotropic systems with different thermodynamic characteristics are chosen as case studies: cyclohexane/ethyl acetate/ethanol (Case 1), ethyl acetate/ethanol/water (Case 2), and n-hexane/ethyl acetate/acetonitrile (Case 3). The task of separating the above three systems is accomplished through the application of both three-column extractive distillation configuration (TCED), and four-column extractive distillation configuration (FCED). The objective functions for multi-objective optimization of all separation configurations include the minimum total annual cost (TAC), the minimum entropy generation and the minimum CO2 emissions. The research findings indicate that when the ratio of theoretical product flowrate at the bottom of the preconcentration column to the feed flowrate is not less than 0.56, or when there is a significant concentration difference among the components in the feed stream, the FCED configuration exhibits clear advantages over the TCED configuration in terms of economic, environmental, and thermodynamic aspects.

The Role of Digital Technologies in Climate Change Management: Strategies for Minimizing the Environmental Impact of USA Companies

The article examines the role of digital technologies in climate change management and reducing the environmental impact of U.S. companies. Innovations such as artificial intelligence, the Internet of Things, and big data are analyzed for their ability to help companies minimize CO2 emissions, optimize resource use, and improve energy efficiency. Challenges companies face when implementing digital solutions: high initial costs, integration difficulties, and regulatory complexities, are discussed. Strategies for overcoming these barriers to achieve sustainable development are proposed.

Dynamic Network-Level Traffic Speed and Signal Control in Connected Vehicle Environment.

The advent of connected vehicles holds significant promise for enhancing existing traffic signal and vehicle speed control methods. Despite this potential, there has been a lack of concerted efforts to address issues related to vehicle fuel consumption and emissions during travel across multiple intersections controlled by traffic signals. To bridge this gap, this research introduces a novel technique aimed at optimizing both traffic signals and vehicle speeds within transportation networks. This approach is designed to contribute to the improvement of transportation networks by simultaneously addressing issues related to fuel consumption and pollutant emissions. Simulation results vividly illustrate the pronounced the effectiveness of the proposed traffic signal and vehicle speed control methods of alleviating vehicle delay, reducing stops, lowering fuel consumption, and minimizing CO2 emissions. Notably, these benefits are particularly prominent in scenarios characterized by moderate traffic density, emphasizing the versatility and positive impact of the method across varied traffic conditions.

Modern Methods of Asbestos Waste Management as Innovative Solutions for Recycling and Sustainable Cement Production

Managing asbestos waste presents a significant challenge due to the widespread industrial use of this material, and the serious health and environmental risks it poses. Despite its unique properties, such as resistance to high temperatures and substantial mechanical strength, asbestos is a material with well-documented toxicity and carcinogenicity. Ensuring the safe removal and disposal of asbestos-containing materials (ACM) is crucial for protecting public health, the environment, and for reducing CO2 emissions resulting from inefficient waste disposal methods. Traditional landfill disposal methods have proven inadequate, while modern approaches—including thermal, chemical, biotechnological, and mechanochemical methods—offer potential benefits but also come with limitations. In particular, thermal techniques that allow for asbestos degradation can significantly reduce environmental impact, while also providing the opportunity to repurpose disposal products into materials useful for cement production. Cement, a key component of concrete, can serve as a sustainable alternative, minimizing CO2 emissions and reducing the need for primary raw materials. This work provides insights into research on asbestos waste management, offering a deeper understanding of key initiatives related to asbestos removal. It presents a comprehensive review of best practices, innovative technologies, and safe asbestos management strategies, with particular emphasis on their impact on sustainable development and CO2 emission reduction. Additionally, it discusses public health hazards related to exposure to asbestos fibers, and worker protection during the asbestos disposal process. As highlighted in the review, one promising method is the currently available thermal degradation of asbestos. This method offers real opportunities for repurposing asbestos disposal products for cement production; thereby reducing CO2 emissions, minimizing waste, and supporting sustainable construction.

Artificial intelligence‐driven sustainability: Enhancing carbon capture for sustainable development goals– A review

AbstractArtificial intelligence (AI) and environmental points are equally important components within the response to local weather change. Therefore, based on the efforts of reducing carbon emissions more efficiently and effectively, this study tries to focus on AI integration with carbon capture technology. The urgency of tackling climate change means we need more advanced carbon capture, and this is an area where AI can make a huge impact in how these technologies are operated and managed. It will minimize manufacturing emissions and improve both resource efficiency as well as our planet's environmental footprint by turning waste into something of value again. Artificial intelligence could be leveraged to analyze huge data sets from carbon capture plants, searching for optimal system settings and more efficient ways of identifying patterns in the available information at a larger scale than currently possible. In addition, AI incorporated sensors and monitoring mechanisms in the supply chain can identify any operational failure at reception itself allowing for timely action to protect those areas. Artificial intelligence also helps generative design for carbon capture materials, which allows researchers to explore new types of carbon‐absorbing material, including metal–organic frameworks and polymeric materials that are important in industrial CO2, such as moisture. In addition, it increases the accuracy of reservoir simulations and controls CO2 injection systems for storage or enhanced oil recovery. Through applying AI algorithms on reservoir geology, production performance and real‐time data this study would like to facilitate the optimization of injection processes as well as minimize CO2 emissions while assuring a maximum efficiency. Artificial intelligence integrates with renewable‐based carbon capture efforts that can be employed by AI‐driven smart grid systems to improve carbon capture methods.

Mild one-pot production of glycerol carbonate from CO2 with separation from ionic liquid catalyst

Carbon capture and utilization stands as way forward in the minimization of CO2 emissions. It complements the search for clean and effective energy, as it is challenging to achieve zero to negative CO2 net emissions. The glycerol carbonate product opens a new strategy based on a double-residue approach (glycerol and CO2) to contribute, together with ionic liquids (ILs), drafting a new one-pot concept (intensified three phase reactor) at mild conditions and using homogeneous catalysts. In this work, the catalytic activity of [P666,14][Br] in the cycloaddition of CO2 to glycidol has been demonstrated to be highly effective under mild conditions of 45 °C and 5 bar. In addition, liquid–liquid extraction approach is found to be an efficient separation strategy for carbonate product and IL catalyst. 1-Decanol (DOH) is selected as efficient extracting solvent, due to its almost complete immiscibility with glycerol carbonate, as well as the high distribution ratio of the IL catalyst in DOH rich phase. It implies an easy product separation and, simultaneously, an easy catalyst recovery. A preliminary kinetic validation of the three-phase reactor performance has been properly described, enabling the intensified concept. Finally, combining COSMO/Aspen simulation and life cycle assessment (LCA) methodologies, low energy consumption (avoiding heating) and low GWP scenarios for the CO2 conversion process have been concluded. This work enables a new concept together with concluding the path to developing this CCU technology in the search of zero to negative CO2 emissions to sustainably capture and convert CO2 into value-added products.

Carbon dioxide emission evaluation of biochar based vegetation concrete for ecological restoration projects

Vegetation concrete is increasingly being used for slope stabilization in highways, green roofing in urban developments, and erosion control in coastal areas due to its sustainable solutions for enhancing landscape aesthetics, mitigating pollution, and protecting the environment. However, the environmental and economic impacts of different mix proportions, particularly concerning CO2 emissions, remain insufficiently explored. This study aimed to identify the optimal mix proportions of vegetation concrete through laboratory testing that minimize CO2 emissions while ensuring compatibility with plant growth and cost-effectiveness. The vegetation concrete mix were prepared using different quantities of biochar (5 %, 10 %, and 15 %) and cement content (4 %, 8 %, and 12 %) by weight to evaluate their impact on porosity, unconfined compressive strength (UCS), alkalinity, plant compatibility, cost-effectiveness, and CO2 emissions from raw materials. The results indicate that increasing the biochar content from 0 % to 15 % led to a 16 % reduction in porosity and a 92 % increase in UCS of vegetation concrete. Similarly, increasing the cement content from 0 % to 12 % resulted in an 11 % decrease in porosity and a substantial 200 % increase in the UCS of vegetation concrete. Moreover, the addition of 5 % biochar had a beneficial effect on plant growth, however, increasing the biochar content beyond this level adversely impacted plant development. Based on laboratory test results, the recommended optimal mix for vegetation concrete includes a mix of 5 % biochar, 8 % low-alkaline sulfoaluminate cement, 6 % sawdust, and 6 % ferrous sulfate. The analysis of CO2 emission of the materials studied showed that cement contributed the most to CO2 emissions, followed by biochar, sawdust, water, and soil. Notably, the utilization of sulfoaluminate cement led to a 31.8 % reduction in CO2 emissions compared to Portland cement, while also lowering the overall cost of vegetation concrete by 24.7–33.8 %. This research underscores the necessity of scientifically establishing relationships between the composition of plant-based concretes and their environmental and economic performance. In summary, this study identifies optimal mix proportions for vegetation concrete, leveraging low-alkaline sulfoaluminate cement and biochar to minimize CO2 emissions, enhance landscape aesthetics, and ensure compatibility with plant growth, thus emphasizing its potential as a sustainable and cost-effective construction material.

Thermo-environmental analysis of a renewable Allam Cycle for generating higher power and less CO2 emissions

Although CO2 emissions from burning fossil fuels have caused numerous environmental problems, the utilization of oxy-fuel cycles has proven to be an effective method for generating more power while minimizing CO2 emissions. In this study, an innovative power generation system was presented that integrates renewable energies (solar and wind) with a small-scale oxy-fuel cycle (Allam Cycle). The system underwent modeling, simulation, and optimization from energy, exergy, exergoeconomic, and environmental perspectives. Generating higher power and emitting lower CO2 emissions compared to conventional gas turbines was the main goal of this study. Additionally, to present results meaningfully, comparisons were made between two different weather conditions: St. Petersburg in Russia (cold climate) and Tehran in Iran (normal climate). In this regard, dynamic analysis was performed using TRNSYS and Engineering Equation Solver (EES) tools. Furthermore, a multi-objective optimization was conducted using the TOPSIS multi-criteria method on functional parameters. The optimization results revealed that the maximum energy efficiency and minimum total cost rate were achieved at 75 % for Tehran and 1.84 $/h for St. Petersburg. Providing power for internal components, the highest power sold to the grid was reported to be 8365 kWh in December in St. Petersburg and 9646 kWh in May in Tehran. Additionally, the flat plate collector (FPC) demonstrated maximum efficiency and stored heat of 36 % in Tehran and 2.25 kWh in St. Petersburg, respectively. Ultimately, the results of pollutant emissions indicated that the Allam Cycle achieved a significant CO2 reduction compared to a conventional gas turbine, with a reduction ratio of 117 times in St. Petersburg and 119 times in Tehran.

Techno-economic analysis and optimization of a hybrid solar-wind-biomass-battery framework for the electrification of a remote area: A case study

An off-grid hybrid energy framework on the basis of wind turbines and photovoltaic panels as the primary source of energy and a biogas generator and energy storage unit as a back-up system, was considered for clean energy generation in a rural area in Semnan, Iran. This study was aimed at designing an optimized hybrid renewable framework with minimal costs and CO2 emissions and highest reliability, utilizing comprehensive modeling based on two energy management strategies. The decision-making variables in this work were the type and number of wind turbines, number of storage units, capacity of solar panels, and the capacity of biogas generator. Then variations in turbine model, inflation rate and biofuel prices and their effect on the optimal design of the hybrid framework was investigated in a range of uncertainties (0, 1, and 2.5 %). Additionally, the powerplant emissions penalty cost rate was also considered. Eventually, the optimal hybrid frameworks were economically and environmentally compared with a stand-alone diesel generator. Optimization results confirmed the superiority of the proposed hybrid wind-solar-biomass-battery system in comparison with other investigated systems. The cost of energy for the optimal design including biogas generator (22 kW), photovoltaic panels (30.7 kW), a 10 kW wind turbine, 11 batteries and an inverter (15.1 kW), while considering the inflation rate of 10 % and the uncertainty rate of 0 %, is equal to 0.201 $/kWh. Additionally, the selected optimal design emitted 97 % less CO2 annually, in comparison with the similarly-sized diesel-based energy system.

Integrating the triple bottom line of sustainability, resilience strategies, and product perishability consideration to design a pharmaceutical supply chain network: A COVID-19 case study

This paper aims to design a resilient and sustainable pharmaceutical supply chain network under the perishability of medicine in which a multi-objective nonlinear mathematical model is formulated. To this end, four objective functions seek to minimize total cost, maximize the social indicators, minimize CO2 emission and minimize de-resilience measures. Moreover, the three main categories of resilience strategies are integrated to mitigate the severe impacts of disruption. In order to solve the model, lexicographic goal programming is applied for small-scale problems, and NSGA-II is utilized for large-scale problems. The applicability of the proposed model is demonstrated by implementing it in a real case study during the COVID-19 situation. Also, a set of sensitivity analyses is conducted to validate the model and show the behavior of the objective functions. The results reveal the superiority of the resilient model with integrated strategies. Eventually, the Pareto front solutions are provided to quantify the trade-offs in satisfying the conflicting objective functions.

Highly efficient Co-added Ni/CeO2 catalyst for co-production of hydrogen and carbon nanotubes by methane decomposition

The catalytic decomposition of methane (CDM) is a hydrogen and nanostructured carbon production process with minimal CO2 emission. Among the transition metal-based catalysts (e.g. Ni, Fe, Co, etc.), Ni-based catalysts are most widely studied due to the higher catalytic activity in decomposing methane. However, the limited lifespan of the catalyst makes it unsuitable for practical applications. Effective methane decomposition catalysts should be designed to optimize both reaction efficiency and catalyst lifetime. A Ni/CeO2 catalyst, developed in previous studies, Co was added to promote low-temperature (< 700 °C) activity manipulating the redox property of Co. Among the prepared catalysts with varying Ni:Co ratio, the methane conversion rate of the Ni8Co2/CeO2 catalyst was approximately twice that of the Ni10/CeO2 catalyst, confirming its excellent low-temperature activity. The reaction rate of Ni8Co2/CeO2 catalyst was 4.38 mmol/min∙gcat at 600 °C with WHSV of 36 L/gcat∙h. In terms of characteristics of carbon products, Raman spectroscopy analysis revealed that the carbon grown on the catalyst surface exhibited high crystallinity, with D-G band ratio (ID/IG) of 1.01. The fresh and used catalyst samples were characterized by TEM, XPS, XAS, and other methods to analyze the parameters affecting catalytic activity.

Solar Power and Demand Response for Greening Indian Lignite Power Plants: A CO2 Reduction Initiative

This research paper delves into the prospect of curbing carbon dioxide (CO2) emissions by strategically deploying solar photovoltaic (PV) systems and orchestrating demand response (DR) mechanisms within Indian lignite power plants (LPP). The study responds to the critical imperative of mitigating greenhouse gas (GHG) emissions originating from coal-based electricity generation, a matter of substantial consequence in the context of climate change. In pursuit of optimal solar PV system allocation, this research employs the particle swarm optimization (PSO) technique, considering a spectrum of factors including solar resource availability, electricity demand patterns, and the CO2 intensity associated with coal power generation. The primary objective is to minimize CO2 emissions while maximizing the integration of solar PV and curtailing power losses, all while accounting for the intermittent nature of solar power and the dynamic nature of demand. The proposed approach is rigorously tested on the IEEE 33 bus system, supplied by the LPP. The results convincingly demonstrate a remarkable reduction in CO2 emissions, amounting to 29.69%, following the implementation of the proposed approach. This research presents a concrete step towards a more sustainable and environmentally friendly energy landscape, offering valuable insights for policymakers and stakeholders in the energy sector.

Modeling, parameter estimation and optimization of fluidized bed-based alternative ironmaking process for CO2 emission reduction

This study presents the development of a comprehensive process model for simulating and optimizing the FINEX process consists of the multi-stage fluidized beds, the melter-gasifier unit, and a gas recycling system. The model is developed using Pyomo, a Python-based open-source software package that provides extensive capabilities for formulating, solving, and analyzing optimization models. It incorporates heat and mass balance equations and captures a wide range of chemical reactions, including the reduction of iron ore by hydrogen and carbon monoxide, the calcination of carbonate materials, the water–gas shift reaction, and coal gasification and combustion. Unknown parameters in the model, such as heat loss in each reactor, the extent of the calcination reaction, and the outlet gas temperature from the melter-gasifier, were estimated to calibrate the model. These parameters were estimated by solving an optimization problem that minimizes the gap between the model and real plant data. The optimized model was employed to investigate various scenarios for minimizing CO2 emissions in the FINEX process. This included assessing the impact of HBI charging rates, iron ore quality, and the integration of a CCUS unit. For each scenario, an optimization problem was formulated to minimize production costs across a range of CO2 tax levels. The optimal solutions revealed the relationships between process economics and CO2 emissions for each variable.

Development and Characterization of Basalt Fiber-Reinforced Green Concrete Utilizing Coconut Shell Aggregates

Lightweight aggregate concrete (LWAC) is gaining interest due to its reduced weight, high strength, and durability while being cost-effective. This research proposes a method to design an LWAC by integrating coconut shell (CS) as coarse lightweight aggregate and a high volume of wet-grinded ultrafine ground granulated blast furnace slag (UGGBS). To optimize the mix design of LWAC, a particle packing model was employed. A comparative analysis was conducted between normal-weight concrete (M40) and the optimized LWAC reinforced with basalt fibers (BF). The parameters analyzed include CO2 emissions, density, surface crack conditions, water absorption and porosity, sorptivity, and compressive and flexural strength. The optimal design was determined using the packing density method. Also, the impact of BF was investigated at varying levels (0%, 0.15%, and 1%). The results revealed that the incorporation of UGGBS had a substantial enhancement to the mechanical properties of LWAC when BF and CS were incorporated. As a significant finding of this research, a grade 30 LWAC with demolded density of 1864 kg/m3 containing only 284 kg/m3 cement was developed. The LWAC with high-volume UGGBS and BF had the minimum CO2 emissions at 390.9 kg/t, marking a reduction of about 31.6% compared to conventional M40-grade concrete. This research presents an introductory approach to sustainable, environmentally friendly, high-strength, and low-density concrete production by using packing density optimization, thereby contributing to both environmental conservation and structural outcomes.

Non-thermal plasma-catalytic processes for CO2 conversion toward circular economy: fundamentals, current status, and future challenges.

Practical and energy-efficient carbon dioxide (CO2) conversion to value-added and fuel-graded products and transitioning from fossil fuels are promising ways to cope with climate change and to enable the circular economy. The carbon circular economy aims to capture, utilize, and minimize CO2 emissions as much as possible. To cope with the thermodynamic stability and highly endothermic nature of CO2 conversion via conventional thermochemical process, the potential application of non-thermal plasma (NTP) with the catalyst, i.e., the hybrid plasma catalysis process to achieve the synergistic effects, in most cases, seems to promise alternatives under non-equilibrium conditions. This review focuses on the NTP fundamentals and comparison with conventional technologies. A critical review has been conducted on the CO2 reduction with water (H2O), methane (CH4) reduction with CO2 to syngas (CO + H2), CO2 dissociation to carbon monoxide (CO), CO2 hydrogenation, CO2 conversion to organic acids, and one-step CO2-CH4 reforming to the liquid chemicals. Finally, future challenges are discussed comprehensively, indicating that plasma catalysis has immense investigative areas.

Analysis of cyclone separator solutions depending on spray ejector condenser conditions

The core design strategy for minimizing CO2 emissions in gas power plant entails combining a spray ejector condenser (SEC) and separator to accomplish steam condensation and CO2 purification. This innovative process involves direct-contact condensation of steam with CO2, facilitated by interaction with a subcooled water spray, along with a cyclone separator mechanism intended for generating pure CO2. The investigation of the SEC section, both experimentally and analytically, provides crucial insights into its operational dynamics. Given the susceptibility of cyclone efficiency to fluctuations in SEC conditions, this research endeavors to examine the impacts of CO2 volumetric flow rate and droplet break-up within the SEC on the separation efficacy of the cyclone separator. Additionally, the impact of cone size on the performance of the cyclone has been investigated. Here, a three-dimensional, transient, and turbulent cyclone separator is numerically simulated using Ansys Fluent 2021 R1. The Reynolds Stress Model is employed to simulate turbulent flow, while a mixture model is utilized to replicate swirl two-phase flow within the separators. The findings revealed that reductions in steam and CO2 flow rates were associated with a decrease in outlet temperature but an increase in SEC inlet temperature, leading to a rise in temperature difference and heat transfer rate. Furthermore, an augmentation in cyclone cone size (from 0.2 to 0.5 m) resulted in enhanced separation efficiency (from 77.30 % to 80.98 %) alongside an elevation in pressure drop (from 6.08 Pa to 10.91 Pa), suggesting a compromise between CO2 purification and energy consumption. Additionally, elevated CO2 flow rates induced a rise in pressure drop and separation efficiency, ultimately achieving maximum efficiency at a rate of 24 gs. Moreover, the exploration into droplet breakup manifesting in a boost in separation efficiency from 50.98 % to 100 % across droplet diameters ranging from 1 to 20 μm.

Closed-loop supply chain network of end-of-life wind turbines mathematical model proposal considering environmental, employment, and cost reduction aspects

The worldwide climate issue makes wind energy an attractive renewable energy source. Wind energy reduces carbon emissions, energy prices, creates jobs, improves power access, and ensures energy security. The mass of employed materials in wind turbines indicates a lot of potential raw materials when the whole mass is evaluated. Additionally, a new wind turbine costs $2–$4 million. Remanufacturing or using recycled raw materials from end-of-life wind turbines is practical due to the decreased cost of new wind turbines. To ensure sustainability, CO2 emissions and social advantages like employment must be considered. Given the typical life expectancy of wind turbines at 20–25 years, this issue must be addressed globally in the mid-term. The objective of the conducted study is to provide a multi-objective mathematical framework that encompasses the environmental, social, and economic aspects of wind turbines at the end of their operational lifespan. Hence, this study presents a methodical framework for achieving sustainability in the wind energy sector through the implementation of a closed-loop supply chain. Furthermore, a lexicographic solution approach is offered for each objective of the mathematical model, with the aim of providing decision makers with a comprehensive overview of solutions. The methodology employed in this work involves the utilization of a mixed-integer linear programming (MILP) approach to address the closed-loop supply chain for end-of-life wind turbines. Additionally, a lexicographic solution approach is provided to effectively solve the problem at hand. The novelty of this article lies in its examination of the closed loop supply chain network for end-of-life wind turbines, encompassing the social, environmental, and economic dimensions. This study represents the first of its kind to explore these aspects comprehensively. The proposed mathematical model has three objectives which cover the remanufacturing and recycling and aims to minimize CO2 emissions, unemployment, and transportation cost. In order to evaluate the proposed model, Türkiye is utilized as case with real-life data and Gurobi 9.1.2 is utilized. The locations of wind turbines could not be collected individually, and Euclidian distance was used instead of road distance due to a lack of data. These are the main limitations of the proposed study. The study revealed that the TEC and TCE lexicographic approach has the best recycling and remanufacturing cost-to-manufacturing cost ratio, 52.54 %. The least desired result is 74.50 % from CTE.

Exergetic efficiency and CO2 intensity of hydrogen supply chain including underground storage

Hydrogen plays a crucial role in the transition to low-carbon energy systems, especially when integrated into energy storage applications. In this study, the concept of exergy-return on exergy-investment (ERoEI) is applied to investigate the exergetic efficiency (defined as the ratio of useful exergy output to invested exergy input) and CO2 equivalent intensity of the hydrogen supply chain, with a specific focus on the underground hydrogen storage process. Our findings reveal that the overall exergetic efficiency of the electricity-to-hydrogen-to-electricity conversion process can reach up to 25 %. Among the hydrogen production methods, green hydrogen, produced via electrolysis powered by renewable energy, exhibits the lowest CO2 equivalent intensity. Blue hydrogen, produced from natural gas with carbon capture, can significantly reduce the carbon footprint of electricity generation, though this advantage comes at the expense of decreased exergetic efficiency. Analysis further indicates that the exergetic efficiency of underground storage components ranges from 72 % to 92 %. A substantial fraction of the exergy is lost during compression and injection of the stored hydrogen. Nevertheless, the subsurface operations contribute a minimal CO2 emission, between 1.46–4.56 grams of equivalent CO2 per megajoule (gr-CO2eq/MJ) when powered by low-carbon energy sources. Furthermore, it is found that hydrogen loss in the reservoir, along with methane and hydrogen leak during surface operations, notably affects the overall efficiency of the storage process.