Minimize CO2 Emissions Research Articles

Impact of Energy Efficient Technologies Combined with Solar Roof Top MPPT Control Towards Achieving Sustainable Development in Indian Household

The primary energy demand in India is expected to increase about 1250 to 1500 million toe(tonne of oil equivalent) in 2030.As a result of increasing incomes and growth in economy, energy demand increases electric energy utility services in lighting, space cooling, industrial production, office automation etc. In 2011, annual average supply was only 0.6 toe per capita; whereas the world average was 1.88 toe per capita. To cater the energy demand with minimum CO2 emissions, two prolonged approach is employed. In the generation side, use of renewable such as solar and wind are promoted. In the customer end, a number of policy measures innovatively taken under Energy Conservation Act 2001.This paper analysis the impact of energy conservation technologies and along with the implementation of Solar Photovoltaic MPPT on the roof top of the Indian household. This combined approach will reduce grid dependency of Indian household and thereby minimizing the peak demand on utility.

Optimal design of a coal-based synthetic natural gas (SNG) process with different coal ranks and gasification technologies via techno-economic analysis

Natural gas is regarded as a cleaner fuel and could be a feedstock for chemicals production. In some regions where petroleum resources are limited, the coal-based synthetic natural gas (SNG) process could be an alternative option. Generally, the coal-based SNG process begins with the gasification of coal to produce crude syngas, implying that coal rank and gasification technology would have a significant impact on the process performance and product quality. Within this work, a series of coal-based SNG processes with different coal ranks and gasification technologies are investigated and analyzed based on rigorous process modeling and simulation. Sensitivity analysis is carried out to optimize key parameters and explore their effects on process performance. The results indicate that the low-rank coal with the Lurgi gasifier could effectively improve the energy conversion efficiency when compared with various combinations of coal ranks and gasification technologies. The lignite coal with the Lurgi gasifier could improve the energy efficiency up to 61% with a Wobbe index of 52 MJ/m3, a carbon yield of 44%, and the minimum CO2 emission of 3.5 kgCO2/kgSNG. The economic evaluation suggests that the total equipment investment for the process based on lignite is 20% and 27% lower than that based on bituminous and anthracite, respectively. Furthermore, the Lurgi gasifier could save 12% equipment investment compared with the Texaco gasifier when the process is based on the same coal rank. The research could provide quantitative guidance for the design and optimization of coal-based SNG processes.

Efficiency of Carbon Dioxide (CO2) Emission Control Target Allocations in China

Effectively allocating emission control targets is critical for China to achieve its emission reduction goals. This study researched the efficiency of total carbon dioxide (CO2) emission control target allocations during the 13th Five-Year Plan period (2015–2020). The efficiency of carbon intensity reduction targets, allocated by the National Development and Reform Commission in 30 regions, was assessed using a Directional Distance Function (DDF) model. Then, a Zero-Sum Gains (ZSG)-DDF model was constructed to determine how to optimally allocate total CO2 emission, under the premise of maximizing economic benefits and minimizing CO2 emissions. The results showed that in the case of fixed total CO2 emissions, to improve the resource allocation efficiency, the quota should be increased in the regions with high efficiencies, and the quota should be reduced in the regions with low efficiencies. The results in this paper can help guide the future allocations of total CO2 emission in China.

A multi-objective optimization approach in defining the decarbonization strategy of a refinery

Nowadays, nearly one quarter of global carbon dioxide emissions are attributable to energy use in industry, making this an important target for emission reductions. The scope of this study is hence that to define a cost-optimized decarbonization strategy for an energy and carbon intensive industry using an Italian refinery as a case study. The methodology involves the coupling of EnergyPLAN with a Multi-Objective Evolutionary Algorithm (MOEA), considering the minimization of annual cost and CO2 emissions as two potentially conflicting objectives and the energy technologies’ capacities as decision variables. For the target year 2025, EnergyPLAN + MOEA has allowed to model a range of 0–100% decarbonization solutions characterized by optimal penetration mix of 22 technologies in the electrical, thermal, hydrogen feedstock and transport demand. A set of nine scenarios, with different land use availabilities and implementable technologies, each consisting of 100 optimal systems out of 10,000 simulated ones, has been evaluated. The results show, on the one hand the possibility of achieving medium-high decarbonization solutions at costs close to current ones, on the other, how the decarbonization pathways strongly depend on the available land for solar thermal, photovoltaic and wind, as well as the presence of a biomass supply chain in the region.

Generation of sustainable models with multi-objective optimum design of reinforced concrete (RC) structures

Structural engineering has different designs, which are needed some requirements for generation suitable modelings. These requirements are related safety of structures and living creatures. Furthermore, some parameters are also evaluated like strength, sustainability, serviceability, etc. The second target should be handled through best modeling namely optimization. Thus, a bridge road can be constructed for cost efficiency or a steel system can be designed to provide the lightest weight. In this study, reinforced concrete beams were handled with rectangular and T-sections to provide carbon emission and total cost decrement. Optimal designs were generated towards different concrete strengths and analyzed for various reinforcement costs. Three metaheuristics were utilized: flower pollination algorithm, Jaya algorithm, harmony search. The best ones were determined about the single-objective application and utilized for multi-objective applications in minimization performance. It could be seen that optimal designs were created for several purposes such as economy, cleanliness, environment friendly, safety via cost, and CO2 emission minimization.

The Capacity of Battery-Electric and Plug-in Hybrid Electric Vehicles to Mitigate CO2 Emissions: Macroeconomic Evidence from European Union Countries

The decarbonisation of the transportation sector is crucial to reducing carbon dioxide (CO2) emissions. This study analyses evidence from European countries regarding achievement of the European Commission’s goal of achieving carbon neutrality by 2050. Using panel quantile econometric techniques, the impact of battery-electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) on CO2 emissions in twenty-nine European Union (EU) countries from 2010–2020 was researched. The results show that BEVs and PHEVs are capable of mitigating CO2 emissions. However, each type of technology has a different degree of impact, with BEVs being more suited to minimizing CO2 emissions than PHEVs. We also found a statistically significant impact of economic development (quantile regression results) and energy consumption in increasing the emissions of CO2 in the EU countries in model estimates for both BEVs and PHEVs. It should be noted that BEVs face challenges, such as the scarcity of minerals for the production of batteries and the increased demand for mineral batteries, which have significant environmental impacts. Therefore, policymakers should adopt environmentally efficient transport that uses clean energy, such as EVs, to reduce the harmful effects on public health and the environment caused by the indiscriminate use of fossil fuels.

The Impact of Green Investment, Technological Innovation, and Globalization on CO2 Emissions: Evidence From MINT Countries

The Mexico, Indonesia, Nigeria, and Turkey (MINT) countries have practiced significant levels of economic growth over the years. However, these countries have not managed to protect their environmental quality in tandem. Thus, the aggravation of environmental indicators traversing these countries radiates a shadow of uncertainty on their achievement of economic growth sustainability. In this regard, green investment and technological innovations are commonly considered as an effective aspect geared to minimize CO2 emissions, as these increase energy efficiency and involve cleaner production. Thus, this study investigates the effect of green investment, economic growth, technological innovation, non-renewable energy use, and globalization on the carbon dioxide (CO2) emissions in MINT countries from 2000 to 2020. After checking the stationary process, this study applied fully modified ordinary least square and dynamic ordinary least square methods to estimate the long-run elasticity of the mentioned regressors on CO2 emissions. The outcomes show that non-renewable energy and technological innovations significantly increase environmental degradation. In contrast, the globalization process and green investment significantly reduce it in the long run. Moreover, the interaction effect of green investment and globalization significantly overcomes the pressure on the environment. Similarly, the moderation effect of technological innovation and globalization significantly reduces the emission level in the region. Moreover, the U-shaped environmental Kuznets curve hypothesis was observed between economic growth and carbon emission across the MINT countries. Furthermore, the findings of the Dumitrescu and Hurlin’s panel causal test disclose that bidirectional causality exists between green investment, globalization, technological innovations, non-renewable energy, and CO2 emissions. This study also recommends some valuable policy suggestions to governments in general and to policymakers specifically which are aimed to endorse environmental sustainability in the MINT countries.

Green environment and circular economy: A state-of-the-art analysis

Structured environments are considered an essential topic in the circular economy application. Circular techniques to purify wastewater and regenerate raw materials should be developed to reduce the environmental effect of industry. Different concentration technologies and energy delivery techniques compared to economically practical and environmentally pleasant treatment systems. Ecological sustainability is one of the world's most important concerns for every person. In particular, Green Environment (G.E.) is defined as economic operations relating to the production, distribution, and use of commodities and services that, to prevent significant environmental risks and ecological scars, contribute to a long-term increase in human well-being in future generations. There are no common specific and quantitative indicators of the environment. For existence as a species in its most significant meaning, the Circular Economy (C.E.) notion is essential. Therefore, it is necessary to acknowledge its fundamental limits because of its critical importance. It mainly aims to save precious resources and minimize CO2 emissions to avoid the GE-CE model from degrading the environment. Although the phrase circular economy is not used openly and often in literature, related notions, such as life cycle research, energy consumption reduction, energy efficiency, and the project's carbon/energy, are evident. The application involves problems such as construction difficulties, durability, and a lifecycle cost-benefit assessment are highlighted. Different study subjects are recommended for practical questions such as design efficiency, construction difficulties, timely maintenance, and a lifecycle evaluation to achieve 94.5%.

Design optimisation of reinforced concrete pile foundation using generalised reduced gradient algorithm

PurposeThe purpose of this study is to find the optimum design of Reinforced Concrete (RC) pile foundation to enable efficient use of structural concrete with greater consequences for global environment and economy.Design/methodology/approachA non-linear optimisation technique based on the Generalised Reduced Gradient (GRG) algorithm was implemented to find the minimum cost of RC pile foundation in frictional soil. This was achieved by obtaining the optimum pile satisfying the serviceability and ultimate limit state requirements of BS 8004 and EC 7. The formulated structural optimisation procedure was applied to a case study project to assess the efficiency of the proposed design formulation.FindingsThe results prove that the GRG method in Excel solver is an active, fast, accurate and efficient computer programme to obtain optimum pile design. The application of the optimisation for the case study project shows up to 26% cost reduction compared to the conventional design.Research limitations/implicationsThe design and formulation of design constraints will be limited to provisions of BS 8004 and EC 7.Practical implicationsSince the minimum quantity of concrete was attained through optimisation, then minimum cement will be used and thus result in minimum CO2 emission. Therefore, the optimum design of concrete structures is a vital solution to limit the damage to the Earth's climate and the physical environment resulting from high carbon emissions.Originality/valueThe current study considers the incorporation of different soil ground parameters in the optimisation process rather than assuming any pile capacity value for the optimisation process.

Modified Harmony Search Algorithm-Based Optimization for Eco-Friendly Reinforced Concrete Frames

Cost and CO2 are two factors in the optimum design of structures. This study proposes a modified harmony search methodology for optimization of reinforced concrete beams with minimum CO2 emissions. The optimum design was developed in detail by considering all possible combinations of variable loads, including dynamic force resulting from earthquake motion. Moreover, time-history analyses were performed, and requirements of the ACI-318 building code were considered in the reinforced concrete design. The results show that the optimum design based on CO2 emission minimization is greatly different from the optimum cost design results. According to these results, using recycled members provides a sustainable design.

Optimization of Columns and Bent Caps of RC Bridges for Cost and CO2 Emission

This paper describes a methodology for optimal seismic design of reinforced concrete 3D columns and bent caps (beams) of bridges. Design variables include compressive strength of concrete, geometry, as well as longitudinal and shear reinforcement of columns and beams. The optimization is performed to minimize the cost and CO2 emissions using the enhanced colliding bodies optimization (ECBO) algorithm. The trade-off between cost and CO2 emissions shows that in the design for minimizing CO2 emissions compared to the design based on the cost minimization, increasing 1.4 % in cost can decrease CO2 emissions by 6.1%.

Modeling of a Grid-Independent Set-Up of a PV/SOFC Micro-CHP System Combined with a Seasonal Energy Storage for Residential Applications

Renewable energy sources based on solar and wind energy provide clean and efficient energy. The intermittent behaviour of these sources is challenging. At the same time, the needs for efficient, continuous and clean energy sources are increased for serving both electricity and thermal demands for residential buildings. Consequently, complimentary systems are essential in order to ensure a continuous power generation. One of the promising energy sources that helps in reducing CO2 emissions, in addition to providing electrical and thermal energy efficiently, is a Solid Oxide Fuel Cell (SOFC) system operated in a combined heat and power (CHP) mode, due to high electrical efficiencies (in full and part load) and the fuel flexibility. Currently, most studies tend to focus on fuel cell model details with basic information about the building’s energy requirements. Nevertheless, a deep understanding of integrating fuel cell micro-CHP systems with renewable energy systems for the residential sector is required. Moreover, it is important to define an operating strategy for the system with a specific controlling method. This helps in evaluating the performance and the efficiency of the building energy system. In this study, an investigation of different configurations of a hybrid power system (HPS) was carried out. The intended aim of this investigation was to optimize a HPS with minimal CO2 emissions, serving the energy demands for a single-family house efficiently and continuously. As a result of this study, a photovoltaic (PV)/SOFC micro-CHP system has satisfied the intended goal, where the CO2 emissions are significantly reduced by 88.6% compared to conventional systems. The SOFC micro-CHP plant operated as a complimentary back-up generator that serves the energy demands during the absence of the solar energy. Integrating the Power to Gas (PtG) technology leads to a similar emission reduction, while the PtG plant provided a seasonal energy storage. The excess energy produced during summer by the PV system is stored in the fuel storage for a later use (during winter). This SOFC micro-CHP configuration is recommended from an energy and environmental perspective. In terms of feasibility, the costs of SOFC based micro-CHP systems are significantly higher than traditional technologies. However, further technology developments and the effect of economy of scale may cause a substantial drop in costs and the micro-CHP shall become economically competitive and available for residential users; thus, enabling a self-sufficient and efficient energy production on site.

Exergoeconomic, carbon, and water footprint analyses and optimization of a new solar-driven multigeneration system based on supercritical CO2 cycle and solid oxide steam electrolyzer using various phase change materials

This study presents an innovative multigeneration for power, cooling load, distilled water, and hydrogen production from solar energy. The proposed system is comprised of a supercritical carbon dioxide (sCO2) ejector refrigeration cycle, a solar still desalination unit (SSDU), and a solid oxide steam electrolyzer (SOSE), integrated with parabolic dish collectors (PDCs) field. Exergoeconomic, carbon footprint (CF), and water footprint (WF) analyses are performed to assess the comprehensive performance of the system using seven inorganic and metal high-temperature PCMs, namely MgCl2, NaCl, LiF-MgF2, NaF-CaF2-MgF2, Zn-Cu-Mg, Cu-Si-Mg, and Cu-Si. It is found that Cu-Si delivers superior thermodynamic performance enhancement, and NaF-CaF2-MgF2 leads to the lowest economic, carbon, and water footprint performances among the desired PCMs. Moreover, multi-objective antlion optimization (MOALO) is conducted to ascertain and compare the maximum exergy efficiency and the minimum product cost, CO2 emission, and water consumption rates of Cu-Si and NaF-CaF2-MgF2. Under optimal conditions, Cu-Si gives an exergy efficiency of 31.27% with hydrogen, net power, cooling capacity, and distilled water production of 44.56 kg/h, 1508 kW, 74.03 kW, and 15.48 kg/h, respectively, and NaF-CaF2-MgF2 yields the lowest cost, CO2 emission, and water consumption rates of 73.55 $/h, 86.338 CO2e/h, and 180.73 kg H2O/h, respectively indicating 11.01%, 5.20% and 7.88% improvements with an 8.98% decrement in exergy efficiency compared with Cu-Si.

Hopfield neural network based on clustering algorithms for solving green vehicle routing problem

As a result of the rapidly increasing distribution network, the toxic gases emitted by the vehicles to the environment have also increased, thus posing a threat to health. This study deals with the problem of determining green vehicle routes aiming to minimize CO2 emissions to meet customers' demand in a supermarket chain that distributes fresh and dried products. A new method based on clustering algorithms and Hopfield Neural Network is proposed to solve the problem. We first divide the large-size green vehicle routing problem into clusters using the K-Means and K-Medoids algorithms, and then the routing problem for each cluster is found using the Hopfield Neural Network, which minimizes CO2 emissions. A real-life example is carried out to illustrate the performance and applicability of the proposed method. The research concludes that the proposed approach produces very encroaching results.

Modeling approach and simulation study to assess the utilization potential of industrial waste heat in district heating systems

Companies and nations are developing strategies to reduce CO2 emissions and achieve their climate goals. Due to their high share of final energy demand, heating and cooling supply systems are of crucial importance regarding emission reduction. Here, especially the industrial and building sector have a dominating influence, accounting for 34% and 33% of the global final energy demand. Aside from reducing the energy demand or integrating renewable energy sources, the utilization of industrial waste heat represents an effective approach to minimize CO2 emissions in heating supply systems. Here, the coupling of the building and industrial sector via district heating systems offers the potential to utilize industrial waste heat. As the utilization is highly dependent on temperature levels and temporal occurrence, modeling approaches to evaluate the site-specific CO2 emission reduction potential must be developed. To consider heterogeneous industrial heating and cooling supply systems as well as the dynamic occurrence of industrial waste heat and energy demands, we present a tailored modeling approach. The approach enables to model configurable industrial heating and cooling supply systems which can be coupled to district heating systems via an industrial heat transfer station. The simulation models consider different energy converters, temperature levels, as well as waste heat sources. Within our research, different configurations are tested through a simulation study and evaluated by an integrated environmental and economic evaluation module. The simulation study, based on industrial thermal load profiles, shows a maximum waste heat utilization of 66% and a corresponding CO2 emission reduction of 39%, with regard to the industrial site’s emissions. The coupling with a district heating system can reduce the industrial site’s operating expenses by up to 14 %. However, the allocation of the overall economic benefit is dependent on the assumed heat price.

Optimum Design of Reinforced Concrete T-Beam Considering Environmental Factors via Flower Pollination Algorithm

The minimum cost designs may be costly for the environment. In that case, the cost is not only a single objective in the design of structures. In that situation, CO2 emission minimization can be considered in the optimum design. In this study, reinforced concrete (RC) T-beams were optimized via flower pollination algorithm for the environmental factor minimizing the CO2 emission of the materials. The investigation was done for 4 cases of CO2 emission values and these cases were compared with 5 cases of the cost ratios of concrete and steel. Due to this investigation, it was found that CO2 emission optimization is effective in the reduction of the CO2 emission value by 4.5% compared to cost optimization. If the ratios of CO2 emission and the cost of steel per concrete are different, the optimum design variables and objectives are different. Also, the use of recycled steel is %.30.24 more effective on the reduction of CO2 emission and saving the planet.

Techno-Economic Analysis of a Process to Convert Methane to Olefins, Featuring a Combined Reformer via the Methanol Intermediate Product

The substantial growth in shale-derived natural gas production in the US has caused significant changes in the chemical and petrochemical markets. Ethylene production of ethane and naphtha via steam cracking is one of the most energy- and emission-intensive activities in chemical manufacturing. High operating temperatures, high reaction endothermicity, and complex separation create high energy demands as well as considerable CO2 emissions. In this study, a demonstration of a transformational methane-to-ethylene process that offers lower emissions using energy optimization and a CO2 minimum-emission approach is presented. The comparisons of different reforming processes suggest that the dry reforming of methane has a negative carbon footprint at low syngas ratios of 1 and below, and that additional carbon emissions can be reduced using integrated heating and cooling utilities, resulting in a 99.24 percent decrease in CO2. A process design implemented to convert methane into value-added chemicals with minimum CO2 emissions is developed.

Sustainable synthesis of integrated process, water treatment, energy supply, and CCUS networks under uncertainty

This paper proposes a novel framework for synthesis, design and innovation of sustainable integrated processes that combines water, energy, waste treatment and carbon capture, utilization and storage (CCUS) networks together with the overall objective to minimize CO2 emissions and wastes under uncertainty. Compared to the earlier deterministic method (Li et al., 2020), uncertainty of parameters, CCUS as well as extended models for the process, water and energy networks have been systematically addressed in this work. The applicability of the extended method is demonstrated through a case study that considers the xylitol production process together with water, energy and CCUS networks under uncertain raw material prices, product prices and demands. The results show that the multi-network synthesis and design method is able to determine the optimal solution with high economic benefits and zero or low CO2 emissions under uncertainty for different scenarios.

A comprehensive multidisciplinary investigation on CO2 capture from diesel engine.

Climate change and global warming are the visible consequences of the increased amount of carbon dioxide (CO2) in the atmosphere. Among the various sources of anthropogenic CO2 emission, the diesel engine has a significant contribution. The development of a reliable system to efficiently minimize CO2 emissions from diesel engines to the safest level is lacking in the open literature. Therefore, a comprehensive multidisciplinary approach has been applied in this paper to investigate the efficacy of the post-combustion carbon capture (PCC) process for the diesel engine. The experiments have been performed on the exhaust of a direct injection diesel engine at five different brake powers with blends of aqueous ammonia (AQ_NH3), monoethanolamine (MEA), N,N-dimethylethanolamine (DMEA), and 1-ethyl-3-methylimidazolium tetrafluoroborate (C2mim BF4) ionic liquid (IL) as an absorbent for CO2 capture. The reaction mechanism of these absorbent with CO2 are also studied by the geometrical, energetical, MESP, frontier molecular orbitals, and NBO analysis using the first-principles density functional theory (DFT) calculations. The maximum CO2 absorption efficiency of almost 97% was achieved for the blend consisting of 67% of AQ_NH3 and 33% of MEA. Moreover, AQ_MEA and blend of AQ_NH3, DMEA, and C2mim BF4 ionic liquid showed 96% and 94% CO2 absorption efficiency, respectively.

REFRIGERATION SUPPLY OF REFRIGERATION CHAMBERS OF SMART INDUSTRIAL REFRIGERATORS WITH NEURON-FUZZY CONTROL SYSTEMS OF FOOD FREEZING PROCESSES

A set of theoretical, methodological and practical problems associated with the creation of an intelligent complex for adaptive control of microclimate parameters, the processes of freezing food products in the refrigerating chambers of an industrial refrigerator has been studied. Accordingly, for this task, the structure of the neural network controller of the refrigeration capacity of refrigeration machines of the process control system of an industrial refrigerator was developed, in which information enters the database, a neural network with a learning module and turns into decision-making modules into management actions on the executive intelligent mechanisms of compressors, condensers, evaporators, minimizing CO2 emissions. A model of refrigeration supply for refrigerating chambers of an industrial refrigerator has been developed, in which the parameters of loading geometry and freezing speed, refrigerant pressure in the evaporator in real time determine the parameters of refrigeration supply for refrigerating chambers. The microclimate of the refrigerating chamber is estimated using a multi-parameter model of a neuron-odd decision tree with two integrated output nodes for classifying the state of refrigeration supply of refrigerating chambers. For the decision maker, an intelligent decision support system has been developed for the main block of the upper level of the process control system for an industrial refrigerator, which differs from analogues in the accuracy of predicting the quality of the frozen product by training the neural network using the backpropagation method. Simulation studies of the neural network control system for the refrigeration supply of refrigerating chambers and the process of freezing products with interchangeable structures proved that the system provides the specified control quality parameters, minimizes CO2 emissions and prevents the loss of taste properties of the frozen product.