Large Plants Research Articles

Dioxin concentrations and congener distribution in biomass ash from small to large scale biomass combustion plants.

Biomass plays a pivotal role in global energy production, with a significant share allocated for industrial heat and power generation. The combustion of biomass generates biomass ash, which is widely utilized as a fertiliser. However, concerns arise regarding the presence of dioxins in biomass ash, which may limit its continued use. Dioxins are toxic compounds listed under the Stockholm Convention due to their persistence and detrimental effects on human health and the environment. This study investigates the dioxin content in biomass ashes produced in various combustion plants with a capacity of 1-50MWth in Estonia, where biomass is widely used for heating and power production. The research encompassed samples from nine biomass combustion plants with varying technical parameters considering both bottom and fly ash. Dioxin concentrations were determined for 7 polychlorinated dibenzo-p-dioxins (PCDDs), 10 polychlorinated dibenzofurans (PCDFs), and 12 dioxin-like PCBs (PCBs). The results indicate that dioxin TEQ content in all samples was well below the European Union's (EU) POP Regulation limit of 5µg TEQ/kg, with most values being at least tenfold lower. However, some samples failed to meet the EU Fertilising Products Regulation's threshold of 20ng TEQ/kg. Notably, fly ash exhibited higher dioxin concentrations than bottom ash. While PCBs were in significant concentrations, PCDDs dominated the overall dioxin TEQ content. This study provides essential insights into the dioxin content in biomass ash and its correlation with current EU regulatory limits. It also highlights the complex distribution of dioxin congeners, particularly PCBs, within biomass ash, emphasizing the continued research's importance.

Techno-Economic Analysis of the Solid Oxide Semi-Closed CO2 Cycle for Different Plant Sizes

Abstract This work presents a detailed techno-economic evaluation of the SOS-CO2 cycle, a hybrid semi-closed cycle with solid oxide fuel cells developed by Politecnico di Milano. for different plant sizes, ranging from large utility plants (400 MWel) to industrial applications (50 MWel). The analysis includes the design and sizing of all the cycle components, including specific design optimization models for the most critical cycle components such as the regenerative heat exchanger and the turbine. Results are compared with the performance of the Allam cycle, an oxy-combustion cycle with higher technology readiness level. The results show that the SOS-CO2 cycle maintains high efficiency over the whole size range thanks to the modularity of the fuel cell and the regenerator which counterbalances the decrease in turbomachine efficiency at small sizes. For the utility scale plant, despite its higher specific investment cost (3761 €/kW vs. 2490€/kW), the SOS-CO2 cycle appears to be competitive with the Allam cycle in terms of cost of electricity (128.4 €/MWh and 127.8 €/MWh of the Allam cycle) thanks to its higher efficiency (68.9% vs. 53.1%). At smaller sizes, the higher efficiency and the lower dependance on the economies of scale make the SOS-CO2 more economically advantageous: for the 50 MWel plant, the cost of electricity of the SOS-CO2 is 175.8 €/MWh vs. 205.8 €/MWh of the Allam cycle.

People, Palms, and Productivity: Testing Better Management Practices in Indonesian Smallholder Oil Palm Plantations

More than 40% of the total oil palm area in Indonesia is owned and managed by smallholders. For large plantations, guidelines are available on so-called ‘best management practices’, which should give superior yields at acceptable costs when followed carefully. We tested a subset of such practices in a sample of smallholder plantations, aiming to increase yields and profitability. We implemented improved practices (weeding, pruning, harvesting, and fertiliser application) in 14 smallholder plantations of 13–15 years after planting in Jambi province (Sumatra) and in West-Kalimantan province (Kalimantan) for a duration of 3 to 3.5 years. During this period, we recorded yields, measured palm leaf parameters and analysed leaf nutrient contents. Yield recording then continued for an additional two years. In the treatment plots, the yields did not increase significantly, but the size of the bunches and the size of the palm leaves increased significantly and substantially. The tissue nutrient concentrations also increased significantly, although after three years, the potassium concentrations in the rachis were still below the critical value. Because of the absence of yield increase and the additional costs for fertiliser inputs, the net profit of implementing better management practices was negative, and ‘business as usual’ was justified financially. Some practices, such as harvesting at 10-day intervals and the weeding of circles and paths, were received positively by those farmers who could implement them autonomously, and were applied beyond the experiment. It is challenging to find and implement intensification options that are both sustainable and profitable, that have a substantial impact on yield, and that fit in the smallholders’ realities. On-farm experimentation and data collection are essential for achieving sustainable intensification in smallholder oil palm plantations.

Direct quenching and tempering to achieve high strength and toughness of GPa-Grade nano-precipitated steel: The effect of precipitation behavior and variant selectivity

In order to satisfy the application requirements for strength and toughness of hydropower steels for large pumped storage power plants. In this study, based on the synergy of deformation and phase transition, the trade-off between strength and toughness was broken, and the GPa-grade nano-precipitated steel was prepared by direct quenching and tempering. The effects of Nb–V–Ti composite nano-precipitation behavior and martensite variant selectivity on the strengthening and toughening mechanisms were highlighted. The results show that directly quenched steel has the optimal comprehensive mechanical properties after tempering at 550 °C. Combining the contributions of various strengthening mechanisms, the increase in the yield strength relative to off-line quenching and tempering is mainly attributed to additional dislocation strengthening (∼33 MPa) and precipitation strengthening (∼174 MPa). Direct quenching enhances the homogeneous nucleation of finer mono- and binary precipitates and their stable growth during tempering, while the ternary precipitates do not suffer from elemental inhomogeneities due to lack of re-dissolution. Furthermore, the high-frequency variants of the dominant close-packed plane group tend to form high-angle grain boundaries with different orientations and planar alignments, derived from the refinement of Bain groups induced by the self-plasticity modulation of martensite transition due to the accumulation of deformed geometrically necessary dislocations and finite dislocation slip.

Methodological proposal to approximate the sectoral impacts of a carbon tax at the regional level – the case of Chile

This study proposes a methodological approach to approximate the sectoral impacts of carbon taxes in the different regions of Chile through the environmental extension of intraregional input – output models. These models are calibrated by regionalizing technical coefficients using an indirect method called Industry-Specific Flegg Location Quotient. Carbon dioxide emissions at the regional and sectoral levels are obtained mainly from regional inventories of greenhouse gases, although in some sectors, they are calculated indirectly from fuel consumption. Then, tax scenarios are proposed that allow simulation of the effects on sectoral prices, levels of production, and emissions at the regional level. The results show that implementing an identical tax rate at the national level has a very heterogeneous impact on the country’s regions, with the regions of Atacama, Antofagasta, Valparaíso, and Biobío being the most affected economies in relative terms since most of the large thermoelectric power plants are located there.

Solutions for the Implementation of Algorithms for Closed-Loop Control of the Frequency and Power of Large Power Plant Units Comprising Once-Through Boilers

Solutions for the Implementation of Algorithms for Closed-Loop Control of the Frequency and Power of Large Power Plant Units Comprising Once-Through Boilers

Pollinators respond positively to urban green space enhancements using wild and ornamental flowers

Abstract Pollinator diversity and abundance are under direct threat from human activities. Despite societal dependence on pollinators for crop production, humankind continues to drive pollinator declines through agricultural intensification and urbanisation. Urban environments can provide refuge to some pollinators. There is a need, however, to understand how pollinator communities can be supported in these areas while also considering human needs. Public green spaces provide a promising avenue to target plant–pollinator conservation measures in an urban setting due to their large area, high abundance and low plant–pollinator biodiversity. We used a paired design to compare the plant community, pollinator community and plant–pollinator interactions of (i) public urban green spaces enhanced with wildflower meadows and pollinator‐friendly ornamental plantings to (ii) control unenhanced urban green spaces. The planting types within enhanced sites were also separately compared. Our results show that not only was the diversity of the overall pollinator community higher in enhanced than control sites, but also the complexity of some wild Hymenoptera and the abundance of Diptera pollinator groups. The negative impact of urbanisation on some pollinator groups was reduced in enhanced sites compared with control sites. Planting both pollinator‐friendly ornamental plants and wildflower meadows significantly increased the visits from Diptera and the diversity in the plant–pollinator community compared with wildflower plantings alone. Our results suggest that enhancing public spaces for pollinators has positive effects on key groups and can help mitigate the impacts of urbanisation. Non‐native ornamental plants can also play a role in enhancing green spaces for pollinators while maintaining their recreational functions. This supports a mixed‐planting approach for improving public green spaces in urban areas for both people and nature.

Investigation of sustainable operation oriented- economic, process and environment based multi-criteria optimization of large scale methanol production plant

Methanol is one of the main raw materials and a building block for many essential chemicals in the industry, and it is widely produced from the synthesis gas. It is used as a fuel, solvent, inhibitor, antifreeze agent, and in producing a variety of chemicals including olefins and paraffin. The global demand for methanol is 70 million tons per year and it is estimated to grow by a factor of 6–8% per year so it is necessary to develop a methanol production process, which is economical, and environment friendly. The multi-objective optimization (MOO) approach is used to optimize the large scale Lurgi two-step methanol synthesis process using the non-dominated sorting genetic algorithm-II (NSGA-II) to find the Pareto front solutions involving the process, economic, and environmental-based objectives. Methanol production rate, total energy consumption, total annual CO2 emissions, and profit are considered as objectives. The five decision variables include syngas molar flow, reactor feed pressure, reactor 1 temperature, reactor 2 temperature, and by-product mass flow in the methanol distillation column. Two constraints are set on ethanol mole fraction in the methanol stream, and methanol mole flow in water to the treatment stream. Three optimization cases (two bi-objective and one tri-objective) are developed. In the Case 1 study, CO2 emissions decreased by 65.83%, the methanol production rate increased by 10.11%, and total energy was reduced by 57.55%. The Pareto ranking is carried out using the Preference Ranking On the Basis of Ideal-average Distance (PROBID) method and the best optimal solution is reported for all cases.

Design Optimization of a Run-Of-River Small Hydropower Plant: A Case Study of Mabula Kapi Site

Prior to construction, small or large hydropower plants undergo feasibility studies, which involve technical, economic, financial, social, and environmental aspects. Technical studies, particularly, include various engineering disciplines. This study sought to conduct a component of technical feasibility studies, focused on design optimization of the turbine-plant capacity size for a run-of-river case study called Mabula Kapi, on Kaombe River, Serenje, Zambia. The study method used computational modelling, simulation, and optimization techniques, and used secondary data from previous site. A prefeasibility study conducted earlier had underestimated the turbine-plant capacity owing to underestimated discharge data, owing to lack of site gauging station. A less accurate discharge estimation method involving transposition from a gauged similar catchment had been used. A later feasibility-level site hydrological study found improved discharge data. Riding on the improved discharge data, this study found the following optimised plant design parameters based on plant design discharge of 6.20 m3/s: Installed capacity of 10.20 MW; Annual energy production of 42 GWh; Capacity factor of 47.1%; 3 x Pelton wheel of 272 rpm speed, 2.1 m diameter, 2-nozzles of diameter 0.16 m each, 21 buckets of 0.58m width each; and 3 x 4 MVA generators of 50Hz, 22-poles, and 11kV.

Conceptual Scenarios of Architectural Adaptation of Novosibirsk Power Plant

This year marks the 100th anniversary of the foundation of the First Large Power Plant in Novosibirsk, a significant industrial building. Today, Novosibirsk is facing the problem of losing its integral cultural and historical environment due to the large number of unused dilapidated historical and architectural monuments. In order to prevent this trend and save the architectural heritage, it is necessary to develop modern strategies for reuse of these buildings.In this study, conceptual scenarios of architectural adaptation of M. I. Kalinin power plant, the object of cultural heritage of regional significance, are put forward through the methods of contextual, retrospective, ideographic analysis. These design solutions are classified by the original image of the building, focused on the transformation of the former industrial building in order to reveal the socio-historical side of the power plant as a symbol of the dynamic rise of the economy and energy industry in Novosibirsk.As a result, among the scenarios developed for the main building of the city's first major power plant, the most appropriate one is chosen in the context of the historical development of Novosibirsk.The conceptual principle of Nuanced combination with the historical context is the most effective among the described scenarios, assuming preservation of the general architectural image of the historical monument with reversible changes in the architectural solutions of the building.

Influence of thinning on carbon storage mediated by soil physicochemical properties and microbial community composition in large Chinese fir timber plantation

BackgroundThinning practices are useful measures in forest management and play an essential role in maintaining ecological stability. However, the effects of thinning on the soil properties and microbial community in large Chinese fir timber plantations remain unknown. The purpose of this study was to investigate the changes in soil physicochemical properties and microbial community composition in topsoil (0–20 cm) under six different intensities (i.e., 300 (R300), 450 (R450), 600 (R600), 750 (R750) and 900 (R900) trees per hectare and 1650 (R1650) as a control) in a large Chinese fir timber plantation.ResultsCompared with the CK treatment, thinning significantly altered the contents of soil organic carbon (SOC) and its fractions but not in a linear fashion; these indicators were highest in R900. In addition, thinning did not significantly affect the soil microbial community diversity indices but significantly affected the relative abundance of the core microbial community. Proteobacteria, Acidobacteria, and Actinobacteria were the dominant bacterial phyla; the relative abundances of Proteobacteria and Acidobacteria were highest in R900, and that of Actinobacteria was lowest in R900. The dominant fungal phyla were Ascomycota, Basidiomycota and Mucoromycota; the relative abundance of Ascomycota was lowest in R900, and that of Mucoromycota was highest in R900. The fungal microbial community composition was more sensitive than the bacterial community composition. The activity of the carbon-cycling genes was not linearly correlated with thinning, and the abundance of C-cycle genes was highest in R900.ConclusionsThese findings are important because they show that SOC and its fractions and the abundance of the soil microorganism community in large Chinese fir timber plantations can be significantly altered by thinning, thus affecting the capacity for carbon storage. These results may advance our understanding of how the density of large timber plantations could be modified to promote soil carbon storage.

RCPI controller-based multilevel multistring grid following inverter for large rooftop PV power plant application

RCPI controller-based multilevel multistring grid following inverter for large rooftop PV power plant application

EFISIENSI PEMANFAATAN PUPUK ANORGANIK MELALUI APLIKASI BOKASHI GAMAL PADA TANAMAN CABAI MERAH BESAR HIBRIDA (Capsicum annuum L.)

The purpose of this study was to determine the efficiency of inorganic fertilizer utilization through the use of bokashi from Gliricidia sepium on large hybrid chili plants and its effects on the physical and chemical properties of the soil as well as plant growth and productivity. This study was conducted in Pakkabba Village, North Galesong District, Takalar Regency, and soil, bokashi, and leaf tissue analysis was carried out at the Soil Laboratory of the Agricultural Assessment and Technology Center (BPTP) Maros. This research was conducted using a Randomized Block Design with 8 treatments: 100% inorganic fertilizer (ZA 650, Urea 250, SP36 500, and KCl 450 kg.ha-1); 90% inorganic fertilizer + Bokashi of Gliricidia sepium 5 t.ha-1; 80% inorganic fertilizer + Bokashi of Gliricidia sepium 10 t.ha-1; 70% inorganic fertilizer + Bokashi of Gliricidia sepium 15 t.ha-1; 60% inorganic fertilizer + Bokashi of Gliricidia sepium 20 t.ha-1; 50% inorganic fertilizer + Bokashi of Gliricidia sepium 25 t.ha-1; Without inorganic fertilizer + Bokashi Gliricidia sepium 30 t.ha-1. The results showed that the application of Gliricidia sepium bokashi did not have a significant effect on the level of soil porosity. The application of Gliricidia sepium bokashi increased the residue of N, P, K soil, C-organic, CEC, and soil pH at the end of the study. Inorganic fertilizer 70% of the recommended dose + 15 t.ha-1 Gliricidia sepium bokashi provided significantly better hybrid large chili productivity compared to other treatments, namely 22.11 t.ha-1. Gliricidia sepium bokashi 30 t.ha-1 without inorganic fertilizer produced chili productivity that was no different from the treatment of 100% inorganic fertilizer. These results indicate that the use of 30 t.ha-1 Gliricidia sepium bokashi can save 100% of inorganic fertilizer (650 kg ZA, 250 kg urea, 500 kg SP-36, and 450 kg KCl) in large chili plantings so that the use of 30 t.ha-1 Gliricidia sepium bokashi without inorganic fertilizer can be an alternative for organic cultivation of hybrid large chili plants.

A model for energy predictions and diagnostics of large-scale photovoltaic systems based on electric data and thermal imaging of the PV fields

The aim of this investigation is the development of robust models for the performance prediction and automatic monitoring of large photovoltaic systems, based on historical and real-time electric and thermal data. This issue is increasingly important due to the worldwide diffusion of large photovoltaic systems and their need to identify and predict failures and malfunctions, in order to promptly assess the convenience of maintenance actions. The present model describes the response to irradiance and temperature conditions of both modules and inverters and also it is able to predict shading conditions able to affect the energy yield. The model has been validated against real electric measurements in 6 large PV plants located in southern Italy and it demonstrated to be able to predict the real time power production within a 4.1 % error. Even more importantly, the model and its comparison with subhourly measurements over several years has demonstrated its effectiveness in detecting downtime conditions caused by inverter or string problems. Simulations and measurements revealed that missed energy production due to electrical grid coupling downtime can exceed 50 % on certain days and that the shading conditions (up to 5 % of the daily energy production) can be easily detected and separated from component problems, thus avoiding false alarms. Finally, the analysis of aerial infrared images allowed to further test the model in failure detection capability, assess the relationship between thermal anomalies and underperformance conditions and in predicting the yearly deterioration rate at the PV plants.

Flexibility assessment and aggregation of alkaline electrolyzers considering dynamic process constraints for energy management of renewable power-to-hydrogen systems

In the energy management of renewable power-to-hydrogen (ReP2H) systems, quantifying the flexibility of alkaline electrolyzers (AELs) to respond to fast load-tracking commands and maintain energy balance across various timescales is crucial. However, the flexibility of AELs is subject to electrochemical, and dynamic heat and mass transfer constraints, making them very different, both physically and mathematically, from conventional power system flexibility resources such as energy storage (ES) and electrical vehicles (EVs). Additionally, in large hydrogen plants (HPs), aggregating the flexibility of multiple AELs is necessary. Despite extensive flexibility-related research on power and integrated energy systems, work on the electrolyzers is limited. To address these gaps, this paper presents a flexibility assessment method for AELs. Considering comprehensive nonlinear dynamic process constraints, we establish a flexibility metric based on the maximal/minimal energy an AEL can provide within varying time windows. This metric is additive, facilitating the aggregation of multiple AELs within seconds. By comparing it with the energy demanded by regulatory commands, the feasibility of load-tracking control can be assessed without time-domain simulation. In cases of infeasibility, the cause and required compensation can also be interpreted. Case studies validate the proposed method, and key factors impacting the flexibility of AELs are quantitatively analyzed.

An efficient and generalisable approach for mapping paddy rice fields based on their unique spectra during the transplanting period leveraging the CIE colour space

As one of the most important staple foods globally, rice sustains nearly half of the world's population. Accurate and timely paddy rice mapping is, thus, essential for rice-related policy-making to ensure food security in the context of anthropogenic, environmental and climate changes. However, paddy rice mapping remains a challenging task since it usually has similar spectral characteristics to other land covers. In this research, for the first time, an entirely new approach, called RiceTColour, was proposed for mapping rice fields within the Commission Internationale de l'Eclairage (CIE) colour space based on their unique spectra during the rice transplanting period as observed in remotely sensed imagery. We demonstrate that transplanted rice fields, representing a mixture of soil, water and rice seedlings, consistently exhibit relatively low spectral values in both SWIR and NIR bands across various geographical locations, leading to their unique dark green colours in the false-colour image composed of SWIR, NIR and Red bands. Based upon this, we transformed these three spectral bands into the CIE colour space where paddy rice was found to be readily and completely separated from the other land covers. Straightforward, but specific classification criteria were established within the CIE colour space to differentiate paddy rice from the other land covers. The proposed RiceTColour, thus, represents a new approach for paddy rice identification, that is mapping paddy rice using the CIE colour space based upon the previous underexplored remotely sensed spectra of paddy fields during the transplanting season. The effectiveness of the proposed method was investigated over five rice-planting regions distributed across different geographical regions, characterised by different climates, rice cropping intensities, irrigation schemes and cultural practices. Specifically, the mapping criteria established in a training site (S1) were directly generalised to the other four sites (S2 to S5) for paddy rice mapping. Experimental results demonstrated that the RiceTColour method consistently achieved the most accurate and balanced classifications across all five sites compared with four benchmark comparators: a SAR-based method, an index-based method and two supervised classifier-based methods. In particular, the RiceTColour method performed relatively stable, producing an overall accuracy exceeding 95% in the training site (S1) as well as the four generalised sites (S2 to S5), which is an encouraging result. Such efficient yet stable rice mapping results across various rice-planting regions suggest a very strong generalisation capability of the proposed RiceTColour method. In consideration of the relatively large planting area of paddy rice fields globally, the proposed parameter-free, efficient, and generalisable RiceTColour method, thus, holds great potential for widespread application in various rice-planting areas worldwide.

Carbon emission trading, carbon efficiency, and the Porter hypothesis: Plant-level evidence from China

Focusing on large thermal power plants in China, we examine the causal effect of carbon emission trading scheme (CETS) on their carbon emission efficiency. Using the staggered installation of CETS pilots as a natural experiment, we show that carbon emission trading significantly improves power plants’ carbon efficiency. Specifically, the CETS leads to a 6.9 % increase in carbon efficiency. The carbon efficiency growth effects are stronger for plants that are not state-owned, large in scale, or plants located in regions with high level of marketization. Moreover, the improvement in carbon efficiency mainly comes from the reduction in carbon emission, not the improvement in power generation level. The mechanism analysis indicates that the implementation of CETS increases the number of granted patents by as much as 23 %, which provides supporting evidence for the Porter hypothesis.

Improving biogas production with application of trimetallic nanoparticle using response surface methods

The potential of trimetallic nanoparticles (TMNPs) to enhance biogas production through microbe-to-microbe interactions and boost biogas yield is evident. This present study employed the central composite design (CCD) of response surface methods (RSM) to determine the optimal conditions of iron-cobalt-zinc TMNPs influenced anaerobic digestion for higher biogas yield. The impact of initial pH (6.6–7.4), TMNPs concentration (0–30 mg L−1), temperature (25-45 °C), and hydraulic retention time (HRT) (0-4 days) were modelled for improved biogas production. The results indicated that the linear model terms of pH and TMNPs concentration, and quadratic model terms of temperature and HRT, significantly affect the biogas production. Linear model terms of TMNPs, temperature, pH, and HRT have significant interactive effects and the numerical analysis identified the best conditions for the evaluated parameters. Ideal anaerobic process settings generated a maximum cumulative biogas production of 3700 mL−1POME than blank (2000 mL−1), corresponding to an 85 % yield. Optimizing the initial pH, TMNPs concentration, temperature, and HRT can significantly improve biogas yield and could be helpful for developing more efficient AD processes at large volume and commercial biogas plants. Promoting TMNPs as catalysts positively improves an AD process towards sustainable biogas production.

Deep Edge-Based Fault Detection for Solar Panels.

Solar panels may suffer from faults, which could yield high temperature and significantly degrade their power generation. To detect faults of solar panels in large photovoltaic plants, drones with infrared cameras have been implemented. Drones may capture a huge number of infrared images. It is not realistic to manually analyze such a huge number of infrared images. To solve this problem, we develop a Deep Edge-Based Fault Detection (DEBFD) method, which applies convolutional neural networks (CNNs) for edge detection and object detection according to the captured infrared images. Particularly, a machine learning-based contour filter is designed to eliminate incorrect background contours. Then faults of solar panels are detected. Based on these fault detection results, solar panels can be classified into two classes, i.e., normal and faulty ones (i.e., macro ones). We collected 2060 images in multiple scenes and achieved a high macro F1 score. Our method achieved a frame rate of 28 fps over infrared images of solar panels on an NVIDIA GeForce RTX 2080 Ti GPU.

A Real-Time Intelligent Valve Monitoring Approach through Cameras Based on Computer Vision Methods.

Abnormal valve positions can lead to fluctuations in the process industry, potentially triggering serious accidents. For processes that frequently require operational switching, such as green chemical processes based on renewable energy or biotechnological fermentation processes, this issue becomes even more severe. Despite this risk, many plants still rely on manual inspections to check valve status. The widespread use of cameras in large plants now makes it feasible to monitor valve positions through computer vision technology. This paper proposes a novel real-time valve monitoring approach based on computer vision to detect abnormalities in valve positions. Utilizing an improved network architecture based on YOLO V8, the method performs valve detection and feature recognition. To address the challenge of small, relatively fixed-position valves in the images, a coord attention module is introduced, embedding position information into the feature channels and enhancing the accuracy of valve rotation feature extraction. The valve position is then calculated using a rotation algorithm with the valve's center point and bounding box coordinates, triggering an alarm for valves that exceed a pre-set threshold. The accuracy and generalization ability of the proposed approach are evaluated through experiments on three different types of valves in two industrial scenarios. The results demonstrate that the method meets the accuracy and robustness standards required for real-time valve monitoring in industrial applications.