Plant Owners Research Articles

A probabilistic framework for the estimation of resilience of process plants under Na-Tech seismic events

Industrial plants are complex structures, highly vulnerable with respect to seismic loading. Past seismic events have demonstrated the devastating impact and huge economic losses that an industrial plant can experience not only due to physical damage of equipment, but also due to interruption of the production processes. In order to quantify these economic losses, plant seismic resilience evaluation is required. The current paper presents a probabilistic process flow-based framework for assessment of industrial plant resilience and economic losses in case of seismic events. Uncertainties are considered in the ability of plant equipment to withstand the perturbation, and also in the recovery process including equipment recovery durations and recovery costs. Monte Carlo Simulation is used to account for the uncertainties of the model. A black carbon plant is used as a case study to show the applicability of the model. Results and capability of the proposed model shows that it can be a useful tool for decision makers, plant owners, insurance companies, emergency managers and plant designers in their decision making process.

Power asymmetries in supply chains and implications for environmental governance: a study of the beef industry

PurposeSupply chain governance constitutes the rules, structures and institutions that guide supply chains toward various objectives, including environmental sustainability. Previous studies have provided insight into the relationship between governance and sustainability but have overlooked two crucial dimensions: power dynamics and the influence of outside actors. This paper aims to address these two gaps by measuring differential power (i.e. power asymmetries) among actors across the supply chain, including external actors.Design/methodology/approachThis paper quantifies power dynamics across the entire chain through a structured survey in which supply chain participants rank their peer’s ability to affect environmental and social outcomes. This paper tests this approach by surveying 200 industry professionals (e.g. feedlot owners, retailers) and external actors (e.g. NGOs) in the US beef sector.FindingsRespondents ranked the most powerful actors as follows: feedlot owners; processing plant owners; and regulatory agencies. Results also revealed that trade associations, retailers and cow–calf producers and ranchers perceive a sense of powerlessness. This study reveals multiple power nodes and confirms a shift in the power structure depending on which indicator respondents considered (e.g. environmental impacts vs employee safety). This study concludes that the buyer–producer dichotomy often used to assess supply chain governance fails to capture the complex dynamics among actors within supply chains.Originality/valueThis study demonstrates a novel approach to measure perceptions of power in supply chains. This method enables researchers to map networks of power across entire supply chains, including internal and external actors, to advance understanding of supply chain governance dynamics. Previous studies have misidentified who governs environmental outcomes in supply chains, and NGOs have overestimated the power of consumers and retailers to influence producers.

Learning by Doing: Insights from Power Market Modelling in Energy Economics Courses

Much of energy economics curricula involves the study of techno-economic aspects of energy systems with an increasing focus devoted to fostering an understanding of the interactions between innovative technologies and adaptive markets. As the interplay of these dynamics and their impacts on market equilibria and outcomes is quite complex, optimization models are well-suited to facilitate their study. This paper presents two exemplary model approaches and associated case studies, which can be employed to study market developments driving long-term adaptations in the portfolio of power-generation assets as well as scheduling problems of individual plant owners with a focus on assessing the impact of changing market conditions on the profitability of investments. The combination of these two modelling approaches constitutes an innovative means of facilitating students’ understanding of how individual decisions of different market stakeholders lead to welfare-maximizing market equilibria under the assumption of perfect competition. The models are discussed along with the experiences acquired employing them in various forms as project assignments. In summary, the integration of modelling exercises and assignments into the curriculum of energy economics courses has proven to be a practical means of reinforcing and broadening lecture material that is both interesting and rewarding for students.

Identifying selected diseases of leaves using deep learning and transfer learning models

Leaf diseases may harm plants in different ways, often causing reduced productivity and, at times, lethal consequences. Detecting such diseases in a timely manner can help plant owners take effective remedial measures. Deficiencies of vital elements such as nitrogen, microbial infections and other similar disorders can often have visible effects, such as the yellowing of leaves in Catharanthus roseus (bright eyes) and scorched leaves in Fragaria × ananassa (strawberry) plants. In this work, we explore approaches to use computer vision techniques to help plant owners identify such leaf disorders in their plants automatically and conveniently. This research designs three machine learning systems, namely a vanilla CNN model, a CNN-SVM hybrid model, and a MobileNetV2-based transfer learning model that detect yellowed and scorched leaves in Catharanthus roseus and strawberry plants, respectively, using images captured by mobile phones. In our experiments, the models yield a very promising accuracy on a dataset having around 4000 images. Of the three models, the transfer learning-based one demonstrates the highest accuracy (97.35% on test set) in our experiments. Furthermore, an Android application is developed that uses this model to allow end-users to conveniently monitor the condition of their plants in real time.

Knowledge Retrieval Model Based on a Graph Database for Semantic Search in Equipment Purchase Order Specifications for Steel Plants

The complexity and age of industrial plants have prompted a rapid increase in equipment maintenance and replacement activities in recent years. Consequently, plant owners are challenged to reduce the process and review time of equipment purchase order (PO) documents. Currently, traditional keyword-based document search technology generates unintentional errors and omissions, which results in inaccurate search results when processing PO documents of equipment suppliers. In this study, a purchase order knowledge retrieval model (POKREM) was designed to apply knowledge graph (KG) technology to PO documents of steel plant equipment. Four data domains were defined and developed in the POKREM: (1) factory hierarchy, (2) document hierarchy, (3) equipment classification hierarchy, and (4) PO data. The information for each domain was created in a graph database through three subprocesses: (a) defined in a hierarchical structure, (b) classified into nodes and relationships, and (c) written in triples. Ten comma-separated value (CSV) files were created and imported into the graph database for data preprocessing to create multiple nodes. Finally, rule-based reasoning technology was applied to enhance the model’s contextual search performance. The POKREM was developed and implemented by converting the Neo4j open-source graph DB into a cloud platform on the web. The accuracy, precision, recall, and F1 score of the POKREM were 99.7%, 91.7%, 100%, and 95.7%, respectively. A validation study showed that the POKREM could retrieve accurate answers to fact-related queries in most cases; some incorrect answers were retrieved for reasoning-related queries. An expert survey of PO practitioners indicated that the PO document review time with the POKREM was reduced by approximately 40% compared with that of the previous manual process. The proposed model can contribute to the work efficiency of engineers by improving document search time and accuracy; moreover, it may be expandable to other plant engineering documents, such as contracts and drawings.

FTA ON SHIP FIREFIGHTING PLANT

"One of the main causes of accidents on board ships is fire. This is because of the presence of high temperature, the excess quantity of flammable oil and other combustible materials. A ship is fitted with various types of fire retardant and firefighting equipment so as to fight any kind of fire and extinguish it as soon as possible before it turns into a major catastrophic situation. Purpose: This paper presents the crisis management on ship’s firefighting plant, Technical data for firefighting plant with foam, Fault Tree Analysis data, Fault tree block diagram, Failure probability and basic events of fault tree, The equations of fire analysis, The method of simulation, The simulation of a fire incident in the firefighting plant, for a ship named ""Mamola Defender"" which is a vessel that provides the supply of marine drilling platforms. The results are: Foam firefighting plant diagram, Locations for firefighting systems, Fire Control and Safety Plans, Fault tree block diagram, The values of probabilities, The relationship between input and output events. The results of simulation for a scenario are: radiant heat distribution of a jet fire, the heat flux, the temperature distribution on different stage of fire, the flame propagations. Conclusions: The methods which used for simulation present a possible fire incident in a plant with many hazardous materials. This method of simulation is useful to assistance plant owners and operators in different events of emergency situations. This methodology is useful to prevent human casualties and important structures."

Utilization of Internet of Things for Automatic Plant Watering System Using Soil Moisture Sensor

The rapid development of technology has resulted in an increasing need for the benefits of technology in all aspects of human life. Technology is felt to be very helpful for humans in carrying out a series of daily activities, including the activities of farmers and chili plant business owners. Watering the plants every day makes the plants grow lush and healthy. However, sometimes these activities are neglected due to time constraints or other activities. To overcome this, it is necessary to build a system that can be used to assist automatic watering activities. This research discusses the use of Internet of Things technology to make automatic plant watering control devices using Android and Arduino UNO. The way this tool works is based on the soil moisture value and the testing temperature. The water pump will function when the soil moisture value is on a value scale of 75% with a temperature of 22 degrees Celsius and a water content >= 300 RH. By using soil moisture and temperature detection sensors, this tool will send a command signal to Arduino so that the relay driver is turned on so that the pump can distribute water as needed. From the tests carried out, it can be concluded that the tool made is able to carry out the function of watering plants automatically as expected. The water pump will automatically turn on if the temperature and soil moisture values match the settings in the system.

Holistic management of a smart city thermal energy plant with sewage heat pumps, solar heating, and grey water recycling

This article introduces a modern thermal energy plant consisting of sewage heat pumps, a biogas boiler, thermal solar collectors, and grey water recycling. It further discusses advanced methods to achieve energy efficiency in the plant operation. The project is a collaboration between the industrial plant designer, the municipal plant owner, and the local academic institution. The article presents the framework for the collaboration. The overall target is to investigate how the experience and competence of the three partners can lead to improved operation using data-driven methods and optimization strategies. The industrial partner can closely follow up on its design and increase its knowledge of artificial intelligence and data-driven methods. The municipal partner is given a “free-of-charge” system review. New knowledge and reduced life cycle costs and emissions are possible outcomes. The academic partner gets access to a “living green laboratory,” a unique dataset, and the opportunity to validate developed models and optimization strategies. The plant represents the state-of-the-art for a medium scaled, local thermal energy production system in an existing building cluster. The design energy and emission targets are presented and compared to the operational results. Though the municipal partner can report good agreement between targets and results, an evaluation of the day-to-day operation identified practical examples of system conditions that Artificial Intelligence may improve. The article concludes with a description of plans for future work and a broader discussion of the impacts of introducing data-driven methods to real-life systems.

Value-Driven System Design of Utility-Scale Airborne Wind Energy

In the current auction-based electricity market, the design of utility-scale renewable energy systems has traditionally been driven by the levelised cost of energy (LCoE). However, the market is gradually moving towards a subsidy-free era, which will expose the power plant owners to the fluctuating prices of electricity. This paper presents a computational approach to account for the influence of time-varying electricity prices on the design of airborne wind energy (AWE) systems. The framework combines an analytical performance model, providing the power curve of the system, with a wind resource characterisation based on ERA5 reanalysis data. The resulting annual energy production (AEP) model is coupled with a parametric cost model based on reference prototype data from Ampyx Power B.V. extended by scaling laws. Ultimately, an energy price model using real-life data from the ENTSO-E platform maintained by the association of EU transmission system operators was used to estimate the revenue profile. This framework was then used to compare the performance of systems based on multiple economic metrics within a chosen design space. The simulation results confirmed the expected behaviour that the electricity produced at lower wind speeds has a higher value than that produced at higher wind speeds. To account for this electricity price dependency on wind speeds in the design process, we propose an economic metric defined as the levelised profit of energy (LPoE). This approach determines the trade-offs between designing a system that minimises cost and designing a system that maximises value.

A stormy future? Financial impact of climate change-related disruptions on nuclear power plant owners

In recent years, extreme weather events potentially caused by climate change have led to several nuclear power plants being shut down for varying periods, and such unplanned outages will become more frequent in the future. Nuclear plants are already finding it difficult to compete in electricity markets due to the availability of cheaper alternatives. We quantitatively assess the additional impact of such extreme weather events on the finances of plant owners using Western Europe as a case study and draw some policy implications for existing owners and those considering new nuclear plants.

Louisiana Carbon Storage Projects Surging on Fatter Tax Breaks and Blue Fuel Visions

Louisiana’s energy companies are racing in a new direction chasing rich tax credits and hopes for blue fuels. An economy built on producing oil and gas and turning them into fuels and chemicals is now the leader, by far, in projects to capture and store carbon dioxide emitted by the massive petrochemical and refining facilities along the Mississippi River. Out of 28 applications for CO2 injection projects in the US, 15 are in Louisiana, said Steve Lee, director of the injection and mining division of the Louisiana Department of Natural Resources, during a recent presentation at the CO2 Conference in Midland, Texas. If all those permit requests lead to operating projects, they would increase the current number of sites globally by 50%, according to an annual survey by the Global Carbon Capture and Storage (CCS) Institute, which supports the growth of storage. The trigger for this uptick was the 2022 US Inflation Reduction Act that includes large increases in the tax credits—which can be paid in cash for up to 5 years to companies that reduce their emissions by capturing CO2 and then injecting it into subsurface storage. In Louisiana, the key number in the bill is the top rate paid for injecting CO2 into sites designed to store it permanently—$85 per metric ton for facilities that begin construction prior to 1 January 2033. For owners of chemical plants, this can go a long way toward paying the high cost of carbon-reducing projects required by investors and lenders, and it opens the door for them to get into the business of selling low-carbon blue fuels. Louisiana, with its vast supplies of CO2 that can be separated out at an affordable cost and vast stores of open pore space in the ground, is a test bed for this hydrocarbon-based energy transition. “Our geology is really good in Louisiana. Also, we have an extensive pipeline network and lots of CO2. Half of our CO2 is from industrial emissions, so it is good for capture,” Lee said. Connecting those resources to create a CO2 storage business also requires large numbers of skilled deal makers, engineers, lawyers, and politicians because what they are building is neither simple nor an easy sell. A recent deal, described in corporate releases, helps show the complexities and motivations required to capture, deliver, and store industrial CO2. It starts at the world’s biggest ammonia-producing facilities in Donaldson, Louisiana, owned by CF Industries. The company wants to rid itself of the title of the state’s largest CO2 emitter with a plan to invest $200 million in a unit that will pull CO2 out of its exhaust streams, remove the water, and compress the gas to its supercritical state for shipment.

Soil Monitoring Equipment and Automatic Springers Using Soil Moisture Sensor on Okra Plant at Ikahi Raya Plantation

In this modern era, automatic plant sprinklers are important for important plants, coupled with the busyness of plant owners in caring for plants, watering plants manually is not practical, because homeowners have to come to the garden and water the plants directly using energy. The owner also cannot leave the plant for a long period of time, because the plant can lack water and cause the plant to wither or even die. Currently, the ease and efficiency of time and energy are the main considerations for humans in carrying out activities. From time to time we are faced with rapid technological developments, making human work easier. Therefore, this tool is designed to use a soil moisture sensor (soil moisture sensor) as a detector of soil conditions on plants by means of sensors detecting wet or dry soil and when the soil is wet the pump is ON to automatically water the plants. Plant owners can also monitor plants with LCD (Liquid Crystal Display) and blynk to know soil moisture conditions and their status through plants. This of course can make plant owners more effective in saving energy and time in caring for plants.

Wind Turbine Fatigue Load Analysis for Derating Control Strategies Aimed at Grid Balancing

With the exponential growth of wind energy worldwide, wind power plant owners are eager to participate in grid balancing, in order to compete with traditional power plant owners. In this way, they take part in frequency control which notably amounts to reacting to grid faults by an adequate injection of power. The latter calls for power reserve. For wind farms, this is achieved by derating methods which consist in extracting from the wind less power than the available power, in order to be able to increase the produced power when needed. The objective of this paper is to compare two wind turbine (WT) derating methods in terms of lifetime damage equivalent load (DEL). The first strategy is based on a modification of the generator torque set point to achieve derating, while keeping the standard pitch angle control. The second strategy achieves derating by pitch control while keeping the standard tip speed ratio set point. Both strategies were simulated in OpenFAST under various wind conditions covering all the WT operating regions. Focusing on having a constant power reserve, the simulation results on a single WT show that the pitch-based strategy is the best, among the two considered ones, when it comes to derating while reducing lifetime DEL.

Development of IoT Smart Greenhouse System for Hydroponic Gardens

This study focused on the development of a smart greenhouse system for hydroponic gardens with the adaptation of the Internet of Things and monitored through mobile as one of the solutions towards the negative effects of the worlds booming population, never ending - shrinking of arable lands, and the effect of climate change drastically in our environments. To achieve the goal of the study, the researchers created an actual hydroponic greenhouse system with completely developing plants, and automation in examining and monitoring the water pH level, light, water, and greenhouse temperature, as well as humidity which is linked to ThingSpeak. The developed SMART Greenhouse monitoring system was tested and evaluated to confirm its reliability, functions, and usability under ISO 9126 evaluation criteria. The respondents who include casual plant owners and experts in hydroponic gardening able to test and evaluate the prototype, and the mobile application to monitor the parameters with the results of 7.77 for pH level, 83 for light, 27.94 deg C for water temperature, 27 deg C for greenhouse temperature, and 75% for humidity with a descriptive result in both software and hardware as Very Good with a mean average of 4.06 which means that the developed technology is useful and recommended. The SMART Greenhouse System for Hydroponic Garden is used as an alternative tool, solution, and innovation technique towards food shortages due to climate change, land shortages, and low farming environments. The proponents highly suggest the use of solar energy for the pump power, prototype wiring should be improved, the usage of a high-end model of Arduino to address more sensors and devices for a larger arsenal of data collected, enclosures of the device to ensure safety, and mobile application updates such as bug fixes and have an e-manual of the whole systems.

Education and Certification of Internal Welding Engineer for Plant Owner

Education and Certification of Internal Welding Engineer for Plant Owner

Dis-adoption of small-scale biogas plants in Vietnam: what is their fate?

Biogas production at a small-scale level through anaerobic digestion has been promoted in Vietnam as an appropriate technology for cooking and animal waste management within rural households. Despite the large number of small-scale biogas plants being built, there is an increase in the reported cases of their dis-adoption. This study attempts to present the state of the art of biogas plants' abandonment issue and reveal the fate of biogas plants. The primary data were collected in Thua Thien Hue province in central Vietnam among owners of small-scale biogas plants selected with the purposive sampling technique. Methods included semi-structured interviews with respondents who abandoned their biogas technology (at least 6months before the primary data collection) (n = 37) and with respondents who continually use it (n = 62). SPSS 25 IBM was used for the binary logit model with 6 independent variables. Using a logistic regression analysis of various cross-sectional data, key forces were uncovered to determine the factors that can influence the abandonment of biogas technology. Results showed that households with more members working on the farm and those more satisfied with the biogas plant maintenance are less likely to abandon it. Respondents provided the information that their biogas plants mainly were not used for any other purpose after dis-adoption (excepting biogas plants used for further storage of human excreta because they were connected to toilets).

A Mechanistic Model for the Predictive Maintenance of Heavy-Duty Centrifugal Fans Operating With Dust-Laden Flows

Abstract Heavy-duty centrifugal fans are employed for the transport of dust-laden flows in industrial plants, for cement or steel production, or in energy production of plants and mine ventilation systems. These applications require the disposal of huge amounts of suspended particles, which can lead to a gradual erosion of the machine parts where impacts take place. The wear of the fan components can lead to premature failure of the machine, threatening human safety and reliability of the whole plant. The assessment of the wear severity of the machine, according to the process parameters, can aid the plant owner in scheduling overhaul operations along with the operative life of the machine. Moreover, through a reliable estimation of the wear severity, fan manufacturers can optimize the whole machine design process, from the material selection to the warranty time assessment. This work aims to develop a semi-empirical method capable of estimating the erosion severity of centrifugal fans employed for heavy-duty operations. A mechanistic model which accounts for the erosion on the blade leading edge is proposed. The model is derived by means of an analytical approach and accounts for a number of operating parameters (i.e.,, fan geometry, fan operating point, particle concentration in the flow, fluid properties, and material erosion resistance). A comparison of the theoretical model to the computational fluid dynamic (CFD) simulation results obtained through the use of multiphase particle tracking is also provided to assess the reliability of the present method.

Operational planning of distributed energy generation for a semiconductor fabrication plant

The semiconductor industry is well known for its high resource consumption due to its clean room usage and power-intensive manufacturing technology. This is a significant financial burden for semiconductor fabrication plant owners who wish to minimize the cost needed to cover the electricity and gas loads for their daily operations. From a historical perspective, those electricity and gas cost reductions have been enabled by more energy-efficient manufacturing technologies. Due to the complexity of factors influencing the energy system of a semiconductor plant, other aspects have often been neglected and also not been studied intensively in the academic literature so far. One possibility for reducing energy costs is the integration of a microgrid as on-site distributed energy resources (DER) may offer a less cost-intensive way to supply the energy needed. Therefore, this paper seeks to answer the following research question: How much expected cost can be saved and risk mitigated at a semiconductor fabrication plant via DER? More precisely, in this paper, a microgrid at a semiconductor fabrication plant that has installed DER with combined heat and power (CHP) applications and demand response (DR) is used to simulate the expected annual energy costs under given uncertain electricity and gas prices at different installed capacity levels. A simulation-based approach is used to evaluate several DER capacity sizes. The results of the simulation show that installations of DER at a semiconductor microgrid can dominate the base case of doing nothing, considering the expected minimized cost, the expected CO2 emissions, and conditional value-at-risk (CVaR). In fact, DER can reduce the expected annual energy bill by up to 6%, whereas annual expected CO2 emissions can even be reduced by up to 22%. Moreover, with DER, a microgrid’s risk is reduced as it can react to market conditions and local demand. Additionally, the true potential of microgrid cost savings can only be enabled when DR is allowed and proves particularly effective when energy prices are more volatile.

Valorization of Dairy By-Products: Efficiency of Energy Production from Biogas Obtained in Anaerobic Digestion of Ultrafiltration Permeates

This study aimed at evaluating the methane potential of two ultrafiltration (UFP) and two diafiltration (DFP) permeates generated during milk protein concentration. The permeates were characterized by a different chemical oxygen demand (COD) ranging from 7610 mg O2/L to 57,020 mg O2/L. The CH4 production efficiency was recorded for 20 days and ranged from 149 to 181 NL/kg CODadded. Moreover, the possibilities of the use of UFP/DFP to produce electricity and heat with a combined heat and power (CHP) unit was analyzed to underline the impact of the implementation of anaerobic digestion on the electric and thermal energy requirements of a dairy plant. It was concluded that the application of anaerobic digestion to UFP and DFP treatments generates the energy required to cover all the large-scale dairy plant energy demands and produce extra income. The amount of permeates generated annually in the analyzed dairy plant will enable the production of approx. 22,699 MWh of electricity and 85,516 GJ of heat. This would require a biogas plant with a 3 MW yield. Additionally, the lactose production from UFP/DFP was considered as an alternative or parallel solution for its management. The study confirmed that the biogas and lactose production from UFP/DFP enables plant owners to adjust a plant’s management towards one of these two solutions.

Financial and functionality analysis of a biogas plant in Bangladesh

Installation of a biogas plant in a rural region has become a viable alternative energy source with a variety of health and environmental advantages. Though Bangladesh has enormous resources for biogas production, biogas energy production is infancy stage in Bangladesh. The study aims to explore the economic aspect of household-level biogas plants and determine the relationship between biogas plant functionality and different factors. For doing this, 300 biogas plant owners were interviewed from fifteen Upazilla in Bangladesh and respondents were chosen by a two-stage random sampling technique. The study shows by measuring partial budgeting, USD 294.80 per year can be earned by a family by introducing biogas plant. Cost-benefit analysis showed that a small biogas plant (USD 143.07/year) was most profitable, followed by a large biogas plant (USD 142.17/year). In discounted cost-benefit analysis, medium size biogas plant was found to be the most beneficial investment, followed by a small size biogas plant. Average NPV, BCR, PBP, and IRR of Biogas plant were USD 1629.11, 1.77, 2.93, and 48% with subsidy where USD 1525.25, 1.77, 3.75, and 43% without subsidy. The measurement of carbon trading also highlights the economic benefit of a biogas plant in Bangladesh. The bivariate relationship between the functionality of biogas plants with different factors highlights that higher educated, trained plant owners with quality mason and follow up services ensured the efficient operation of the biogas plant.