Plant Location Research Articles

Health risk assessment of municipal solid waste incineration emissions based on regression analysis

This study examined the potential health risks posed by the operation of 96 waste-to-energy (WtE) plants in 30 cities in the Bohai Rim of China. Utilizing a sophisticated simulation approach, the Weather Research and Forecasting (WRF) model coupled with the California Puff (CALPUFF) model, we obtained the spatial distribution of pollutants emitted by WtE plants in the atmosphere. Hazard indices (HI) and cancer risks (CR) were calculated for each plant using the United States Environmental Protection Agency's recommended methodologies. The results indicated that both HIs and CRs were generally low, with values below the accepted threshold of 1.0 and 1.0 × 10−6, respectively. Specifically, the average HI and CR values for the entire study area were 2.95 × 10−3 and 3.43 × 10−7, respectively. However, some variability in these values was observed depending on the location and type of WtE plant. A thorough analysis of various parameters, such as waste composition, moisture content, and operating conditions, was conducted to identify the factors that influence the health risks associated with incineration. The findings suggest that proper waste sorting and categorization, increased cost of construction, and elevated height of chimneys are effective strategies for reducing the health risks associated with incineration. Overall, this study provides valuable insights into the potential health risks associated with WtE plants in the Bohai Rim region of China. The findings can serve as useful guidelines for law enforcement wings and industry professionals seeking to minimize the risks associated with municipal solid waste (MSW) management and promote sustainable development.

Optimum Design on Integration Biogas into Fuel Cell System in Malaysia: Economic and Environmental Aspect

Transition to renewable energy in electricity generation need a serious study in variety of issues. Environment and economic perspective become the vital issue that need to study in local condition for penetration new concept of energy resources. Feeding a biogas into the fuel cell system seems an attractive way to less dependent on fossil fuel-based electricity generation. This study aims to optimize the plant location based on two aspect environment and economic. Capacitated Plant Location Model (CAPLOC MODEL) were used in determine the optimized location of the plant. The optimum design of power plant in Sarawak solely generated 847.91 MW and Pahang about 604.26 MW. Hopefully, the outcome of the study can provide the information for stakeholder in choosing the location for future energy system in Malaysia.

A System and Method for Retrieving Energy Potential through Irrigation Gate of an Agricultural Dependent Hydro Power Plant

The modern economics depends on the electrical energy and the share of decarburized electrical energy is the base of 2050 net zero emission targets. Non-fossil fuel and fossil fuel energy resources are the integral part of net zero energy system. The net zero emission activities are affected by increasing demand of electricity and its related CO2 emission in 2021. The contribution of 41.4% of renewable energy as compared to 56.8% of non-renewable energy in the year of 2023 at national level necessities attention of green sources of energy like solar, wind, hydro, waste to energy, small hydropower, etc. Hydropower is one of the reliable backbones of clean energy system which should be used effectively for power generation. The turbine dependent hydropower plant of 45-60 years old requires renovation, modernization, uprating and digitization (RMUD) to resolve socio-economic concerns and increasing demands. This scenario imposes the need of finding solution in existing hydropower plant than investing on new unit. Both high and low discharge should be potentially used for power generation to improve the capacity factor of the plant. Therefore, present work focusses on improving capacity factor of an existing agricultural dependent hydro power plants having reaction type turbine. Reaction turbines operates at high flow and low/medium head conditions. The operational conditions of low flow and low head for reaction turbines may lead to noise, vibration and directed to major catastrophic failure of turbines. There is a need of utilizing the energy potentials balancing power generation and irrigation even during low flow releases. Thus, this paper is an attempt to a design and develop a modified irrigation gate with sliding gate and mini hydropower system at the inlet of existing irrigation diversion gate. Stochastic nature of flows through different hydraulic structures is to be addressed by this modification. A case study of an irrigation dependent hydropower plant Dhom (2X1 MW), MAHAGENCO, Satara, Maharashtra having TWO irrigation gates have been considered. In addition to 2MW, the suggested design can generate 680kW by average irrigation releases of 2.47m3/sec through each gate. The suggested model can be used on any hydropower plant and canal locations.

Geographic location of nuclear power plants: love thy neighbour as thyself?

ABSTRACT Countries have incentives to geographically locate high-risk industries outside their own jurisdiction or close to the borders with neighbours. Nuclear power-plants (NPPs) are a good example of high-risk economic activity. The average distance of existing NPPs from neighbouring country borders was only 41 miles, implying that the location planning of NPPs considers the host country’s efforts to impose negative externality on neighbours. Empirical analyses indicate that countries choose to place the NPPs away from their major cities and close to the borders with another country. The distances of NPPs from borders are affected by the riskiness of the plants and the relative sizes of the countries.

GIS based spatial decision-making approach for solar energy site selection, Ardabil, Iran

Fossil fuel emissions have caused immense harm to the environment, making renewable energy sources like solar power essential. However, finding the optimal location for a solar power plant requires multi-criteria decision analysis (MCDA) due to various factors influencing the selection process. This study used the AHP method to weigh criteria such as GHI, Temperature, Elevation, Slope, Land cover, Distance from city, and Distance from road. The layers created from satellite imagery were combined using algebraic sums to produce a final map with 9 classes The analysis showed that class 9 has the most desirable values for each criterion, indicating the most suitable regions for a solar power plant. The results of the study have identified the southern and some central regions of Ardabil province as being the most suitable location for the construction of a solar power plant. These regions have been found to have favorable values for the criteria studied, indicating a higher potential for solar energy generation. Based on the criteria assigned to class 9, the best lands have been identified, occupying a total area of 3085 hectares. This area represents approximately 0.17% of the total area of Ardabil province. These findings highlight the importance of careful site selection for solar power plants to ensure maximum efficiency and sustainability.

GIS-AHP APPROACH FOR A COMPREHENSIVE FRAMEWORK TO DETERMINE THE SUITABLE REGIONS FOR GEOTHERMAL POWER PLANTS IN IZMIR, TÜRKİYE

Geothermal energy is gaining more reputation and importance around the world. Correspondingly, suitable location selection is a critical step and has become necessary for the successful installation and operation of geothermal power plants. This study investigated suitability of İzmir region, located in the Aegean part of Türkiye, in terms of geothermal power plants applications by using the combination of Geographical Information System and Analytic Hierarchy Process. Based on the request of power plants, thirteen important criteria were evaluated under three main categories named as physical (C1), environmental (C2) and technical (C3). Moreover, expert’s opinions were taken into consideration to calculate the importance of these criteria. Key results showed that İzmir was suitable for geothermal power plants. The final suitability map layer pointed out that %8.73 (1.037 km2) of total area were determined as highly suitable regions in terms of installation. In addition, the obtained suitability map layer was compared with actual geothermal power plants. Based on the comparison study, power plants in Seferihisar were moderately suitable for geothermal power plants while the location of Balçova power plant was highly suitable. Regarding the suitability assessment in the present study, the location of Dikili power plants had the least suitability score.

Forecasting Tree Root Architecture as a Complement to Proactive Urban Green Space Design

Abstract Background Stewarding newly planted urban trees to maturity involves consideration of above- and belowground factors. While landscape architects and urban planners often focus on aboveground tree aspects, understanding root structure and function is crucial, especially in urban areas with limited underground space and diverse soil conditions. To address the importance and challenges of belowground planning for urban tree roots, we propose a protocol for site assessment and demonstrate root growth forecasting as a complement to existing urban planning approaches. Methods This paper describes adaptations to a plant root architecture simulator, RootBox, and its subsequent application in 4 scenarios created to assess its efficacy as a complement to the phase of urban planning that prescribes vegetation type and planting location in the context of a myriad of other site considerations. RootBox was parameterized based on observed conformity of root growth simulations to generalized tree root architecture reported in the literature. Results Root growth forecasts for each scenario illustrate that plausible urban tree root system architectures—specifically, commonly observed root growth habits—can be produced by RootBox.In-situroot detection (e.g., with an air spade or hydro-vac) at predetermined time intervals can provide model validation and the opportunity to backward reconfigure RootBox parameters for forecast accuracy. Conclusion The adapted RootBox is a flexible, proof-of-concept solution for predicting urban tree root architecture, considering the site and soil conditions pre-established in the planning process. When used with other relevant tools, RootBox can offer valuable insights during the design or rejuvenation of urban spaces.

Profitability Model of Green Hydrogen Production on an Existing Wind Power Plant Location

This paper presents a new economic profitability model for a power-to-gas plant producing green hydrogen at the site of an existing wind power plant injected into the gas grid. The model is based on a 42 MW wind power plant, for which an optimal electrolyzer of 10 MW was calculated based on the 2500 equivalent full load hours per year and the projection of electricity prices. The model is calculated on an hourly level for all variables of the 25 years of the model. With the calculated breakeven electricity price of 74.23 EUR/MWh and the price of green hydrogen production of 99.44 EUR/MWh in 2045, the wind power plant would produce 22,410 MWh of green hydrogen from 31% of its total electricity production. Green hydrogen injected into the gas system would reduce the level of CO2 emissions by 4482 tons. However, with the projected prices of natural gas and electricity, the wind power plant would cover only 20% of the income generated by the electricity delivered to the grid by producing green hydrogen. By calculating different scenarios in the model, the authors concluded that the introduction of a premium subsidy model is necessary to accelerate deployment of electrolyzers at the site of an existing wind power plant in order to increase the wind farm profitability.

Water Footprints of Dairy Milk Processing Industry: A Case Study of Punjab (India)

A robust assessment of water used in agriculture, including livestock production systems and supply chains, is critical to inform diversification and the development of productivity and sustainable food production systems. This paper presents a detailed analysis of water used and consumed in nine dairy milk processing plants spread across Punjab, India’s leading dairy milk-producing state. Over the five years (2015–2019), the direct water use (DWU) was quantified at 3.31 L of groundwater per kg of milk processed. Only about 26% of the direct water used was consumed, including evaporative losses in various milk processing operations, while the remaining 74% was returned as effluent discharges. The average total water footprint (TWF), accounting for both direct and indirect water consumption, was quantified at 9.0 L of water per kg of milk processed. The majority share (~89%) of the total water footprint was contributed by the indirect water footprint associated with the consumption of electricity (energy) in dairy milk processing activities. The plant’s milk processing capacity and processing products mix also affected significant seasonal and annual variations in the direct and indirect water footprints of dairy milk processing. The analysis also found an inverse relationship between the average total water footprint and the average monthly amount of milk processed in the study plants. Therefore, efforts to reduce the indirect water footprint (associated with energy consumption), the treatment and recycling of effluent discharges, and the optimization of milk processing capacity, the dairy processing product mix, and the locations of dairy processing plants are expected to help reduce the water footprint of dairy processing in the state.

Industrial-scale bioremediation of a hydrocarbon-contaminated aquifer’s sediment at the location of a heating plant, Belgrade, Serbia

Industrial-scale bioremediation of a hydrocarbon-contaminated aquifer’s sediment at the location of a heating plant, Belgrade, Serbia

Optimal location of a biomass power plant in the province of Granada analyzed by multi-criteria evaluation using appropriate Geographic Information System according to the Analytic Hierarchy Process

Nowadays renewable energies are in a period of growth, which favours the birth of numerous researches like, for example, this study about the analysis of the optimal location of a biomass power plant in the province of Granada (Spain). So, the study will be developed using Geographic Information System (GIS) and Multi-Criteria Evaluation (MCE) according to the Analytic Hierarchy Process (AHP). And the main target will be to determine the welcome capacity of the territory to this type of plants.

Renewable energy production over the Iberian Peninsula: Optimizing power plants location and energy reservoirs.

In this paper we present a methodology for obtaining a energy production system based on renewable energy able to generate the energy demand supported by hydro electric pumped storage. This model is based on the minimization of the variance of the differences between energy demand and production. The minimization procedure uses the simulated annealing, and looks for the complementarity (spatial and temporal) of solar and wind resources. The minimized variance is controlled by the use of pumped-storage hydroelectricity. The renewable energy time series are obtained by using pseudo-real climatological series coming from a Regional Climate hindcast (10km spatial resolution, 1 hour temporal resolution) for the period 1959-2007 over the Iberian Peninsula. Once the optimal series are obtained, it is analysed the requirements that the hydro-electrical system must have. The exercise here presented gives an idea of the relationships between the renewable energy production, hydro-electrical power (pumping) and energy storage needed to guarantee the energy demand at long-term under the consideration of no loss of energy.

CoCO2-MOSAIC 1.0: a global mosaic of regional, gridded, fossil, and biofuel CO2 emission inventories

Abstract. Gridded bottom-up inventories of CO2 emissions are needed in global CO2 inversion schemes as priors to initialize transport models and as a complement to top-down estimates to identify the anthropogenic sources. Global inversions require gridded datasets almost in near-real time that are spatially and methodologically consistent at a global scale. This may result in a loss of more detailed information that can be assessed by using regional inventories because they are built with a greater level of detail including country-specific information and finer resolution data. With this aim, a global mosaic of regional, gridded CO2 emission inventories, hereafter referred to as CoCO2-MOSAIC 1.0, has been built in the framework of the CoCO2 project. CoCO2-MOSAIC 1.0 provides gridded (0.1∘ × 0.1∘) monthly emissions fluxes of CO2 fossil fuel (CO2ff, long cycle) and CO2 biofuel (CO2bf, short cycle) for the years 2015–2018 disaggregated in seven sectors. The regional inventories integrated are CAMS-REG-GHG 5.1 (Europe), DACCIWA 2.0 (Africa), GEAA-AEI 3.0 (Argentina), INEMA 1.0 (Chile), REAS 3.2.1 (East, Southeast, and South Asia), and VULCAN 3.0 (USA). EDGAR 6.0, CAMS-GLOB-SHIP 3.1 and CAMS-GLOB-TEMPO 3.1 are used for gap-filling. CoCO2-MOSAIC 1.0 can be recommended as a global baseline emission inventory for 2015 which is regionally accepted as a reference, and as such we use the mosaic to inter-compare the most widely used global emission inventories: CAMS-GLOB-ANT 5.3, EDGAR 6.0, ODIAC v2020b, and CEDS v2020_04_24. CoCO2-MOSAIC 1.0 has the highest CO2ff (36.7 Gt) and CO2bf (5.9 Gt) emissions globally, particularly in the USA and Africa. Regional emissions generally have a higher seasonality representing better the local monthly profiles and are generally distributed over a higher number of pixels, due to the more detailed information available. All super-emitting pixels from regional inventories contain a power station (CoCO2 database), whereas several super-emitters from global inventories are likely incorrectly geolocated, which is likely because regional inventories provide large energy emitters as point sources including regional information on power plant locations. CoCO2-MOSAIC 1.0 is freely available at zenodo (https://doi.org/10.5281/zenodo.7092358; Urraca et al., 2023) and at the JRC Data Catalogue (https://data.jrc.ec.europa.eu/dataset/6c8f9148-ce09-4dca-a4d5-422fb3682389, last access: 15 May 2023; Urraca Valle et al., 2023).

GIS- and AHP-based Decision Systems for Evaluating Optimal Locations of Photovoltaic Power Plants: Case Study of Republic of North Macedonia

This study employs a geographic information system (GIS) and an analytical hierarchy process (AHP) to identify optimal locations for photovoltaic (PV) solar farms in the Republic of North Macedonia. It assesses land suitability using six criteria (solar irradiance, aspect, slope, distance from power lines, roads, and urban areas) and six constraints (urban settlements, agricultural zones, national parks, water bodies, steep slopes, elevations above 1500 m). A suitability map was created using a matrix of pairwise comparisons, and the weights for each criterion were calculated. The map was divided into four categories: highly suitable, suitable, less suitable, and unsuitable. The results showed that 11.6% of the study area was classified as being highly suitable, 40.1% as suitable, 3.6% as less suitable, and 0.8% as unsuitable. Additionally, restricted areas (comprised of national parks, residential and agricultural lands, elevations above 1500 m, and water surfaces with 1000 m buffer zones) accounted for 43.7% of the study area. Utilizing just 0.6% of highly suitable land for PV technology could generate approximately 2870 GWh annually, enough to meet the average electricity needs of the industrial sector across the eight administrative regions of R. N. Macedonia. The study offers a replicable GIS-based approach for solar energy planning, contributing to sustainable development and providing insights for integrating solar PV systems into the national energy strategy.

Optimal generation expansion planning model for solar PV generation on Run of River-based hydro-based power system using binary genetic algorithm

Abstract This paper presents a binary genetic algorithm approach developed to solve the optimal generation expansion planning problem for solar photo-voltaic (PV) based Run of River (ROR) hydro generation systems. The results show that the methodology effectively solves mixed-integer, constrained nonlinear generation expansion problems. The model is then used to optimize the generation expansion problem for the electricity grid in the eastern region of Nepal, considering the under construction and planned solar PV and ROR-based hydropower plants. The optimal problem is formulated to determine the optimal number, location and type of power plant to fulfill the demand for the electricity grid of the eastern zone grid of Nepal. Results show that the total generation from the optimal sites combining hydro and solar sites is found to be about 26% of that of total energy in the Nepalese grid. However, the total demand in the system under consideration for the year is about 1352 GWh. With eliminating line losses, a surplus generation of about 1377 GWh has been found. This surplus can be used as power pooling with neighboring countries. The system has a surplus during the wet season but needs to be improved during the dry season. Hence, appropriate policies can be formulated for power banking and pooling to balance the demand–supply in the system.

The internal geography of firms

We document that plants belonging to small and mid-sized firms are geographically concentrated, while large firms are much more dispersed. These differences are sizable; firms with 2 plants have a dispersion that is 5 log points lower than predicted by industry location patterns, while the corresponding figure is less than 2 log points for firms with 40 plants and less than a half log point for firms with 100 or more plants. These patterns are qualitatively robust across industries, time periods, and alternative specifications. We also find that plants that are farther from the firm headquarters employ less workers than closer plants within the same firm, and that this relationship is attenuated in large firms. We interpret these findings through the lens of a model of plant location in which more productive firms endogenously choose to lower their cost of geographic expansion.

A multivariable model for identification of preferred location of wind plants

Unique features of renewable energies such as wind energy has caused increasing demands for such resources. In order to use wind energy as a natural resource, environmental circumstances and geographical location related to wind intensity must be considered. Different factors may affect on the selection of a suitable location for wind plants. These factors must be considered concurrently for optimum location identification of wind plants. This article presents an integrated approach for location of wind plants by Data Envelopment Analysis (DEA). Furthermore, an integrated DEA approach incorporating the most relevant parameters of wind plants is introduced.The prescribed approach is tested for twenty five different cities in Iran. This is the first study that considers an integrated DEA approach for geographical location optimization of wind plants. Implementation of the proposed approach would enable the energy policy makers to select the best possible location for construction of a wind power plant with lowest possible costs.

Ensemble Bayesian Model Averaging Projections of Wind‐Speed Extremes for Wind Energy Applications Over China Under Climate Change

AbstractWind energy has grown rapidly in recent years as a measure to control carbon emissions and mitigate climate change. Extreme wind can damage wind turbines, cause losses to wind power plants, limit economic benefits of wind energy facilities, and disrupt regional grid balance. Therefore, an accurate assessment of extreme wind speeds at wind turbine hub height and their spatiotemporal variation under climate change is critical for the planning of wind energy and for guaranteeing regional energy security. In this study, the 100‐m extreme wind speeds in China are estimated using an empirical downscaling and Bayesian model averaging ensemble method with the latest ERA5 reanalysis and 20 global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). Two shared socioeconomic pathways (SSP), that is, SSP2‐4.5 and SSP5‐8.5, are considered to account for the uncertainty in anthropogenic emissions. According to the results, the highest extreme wind speeds are primarily found in Inner Mongolia, northeast China, western Tibet, and the eastern coastal region. Extreme wind speeds in central and southeastern China are projected to increase by approximately 2% in the middle (2031–2060) and the end (2071–2100) of the 21st century relative to the baseline period (1985–2014). Summer extreme wind speeds in northwestern Tibet are expected to increase by more than 9% at the end of the century. The findings of this study indicate that it is important to take the present and projected changes in local wind extremes into account when choosing locations for wind power plants and wind energy installations.

МЕТОДИКА ВЫБОРА ОПТИМАЛЬНОГО МЕСТА РАСПОЛОЖЕНИЯ СОЛНЕЧНОЙ ЭЛЕКТРОСТАНЦИИ

Relevance Justification for the location of a solar power plant, presented in this article, is a current and promising task for the design of a solar power plant intended for hydrogen production, as part of the decarbonization strategy and transition to a low-carbon economy. Aim of research The aim of research is the selection and justification for the location of a solar power plant intended for hydrogen production as part of the decarbonization strategy in Russia. Research methods During the research, various analysis and modeling methods were used, including the SAM consultation system and other software tools. Results The article discusses the issues of choosing the optimal location for installing a power plant, determining its configuration and energy management. The practical significance of the work is to create a basis for the design and implementation of efficient and sustainable solar power plants for hydrogen production, which can help achieve decarbonization goals in Russia and improve the country's energy security. Acknowledgments: The research was carried out using a grant from the President of the Russian Federation in 2022–2024 for young scientists and graduate students carrying out promising scientific research and development in priority areas of modernization of the Russian economy. Grant recipient is Regina Tagirovna Khazieva. The priority direction of modernization of the Russian economy (direction of competition No. SP-2022 of the Grant Council of the President of the Russian Federation) is «Energy Efficiency and Energy Saving, Including Issues of Developing New Types of Fuel». Research topic: «Development and Research of a Filter Compensating Device to Improve the Quality of Electricity».

Development of a sampling protocol for collecting leaf surface material for multiphase chemistry studies.

Plant leaves and water drops residing on them interact with atmospheric oxidants, impacting the deposition and emission of trace gases and mediating leaf damage from air pollution. Characterizing the chemical composition and reactivity of the water-soluble material on leaf surfaces is thus essential for improving our understanding of atmosphere-biosphere interactions. However, the limited knowledge of sources and nature of these chemicals challenges sampling decisions. This work investigates how sampling variables and environmental factors impact the quantity and composition of water-soluble material sampled from wet leaves and proposes a flexible protocol for its collection. The ratio of solvent volume-to-leaf area, the solvent-to-leaf contact time, and environmental parameters - including the occurrence of rain, plant location and its metabolism - drive solute concentration in leaf soaks. Despite minor variations, UV-vis absorption spectra of leaf soaks are comparable to authentic raindrops collected from the same tree and share features with microbial dissolved organic matter - including overall low aromaticity, low chromophore content, and low average molecular weight. In addition to guiding the development of a sampling protocol, our data corroborate recent hypotheses on the amount, origin, nature, and reactivity of water-soluble organics on wet leaves, providing new directions of research into this highly interdisciplinary topic.