Plant Operators Research Articles

Evaluating the Impact of Transitioning Maintenance Strategy from Reactive to Proactive in Power Generation Companies: An Empirical Analysis

This empirical study rigorously investigates the impact of transitioning from a reactive maintenance strategy to a proactive approach within the context of power generation companies. The central aim is to quantify and provide a comparative analysis of the efficiency, cost implications, and overall operational impact of adopting proactive versus reactive maintenance strategies in a power plant setting. Drawing on meticulously collected data, the research considers an array of key performance indicators, including maintenance costs, equipment breakdowns, downtime duration, total power output, equipment lifespan, safety incidents, regulatory compliance violations, and investment in staff training and predictive maintenance tools. The findings of this study are both revealing and quantitatively substantial. A transition to a proactive maintenance strategy has demonstrated a reduction in maintenance costs by approximately 20%, coupled with a 35% decrease in the number of equipment breakdowns. Downtime duration was significantly reduced by 40%, enhancing operational efficiency and power output. Notably, the total power output increased by 15%, and the equipment lifespan was extended by an average of 25%. Furthermore, a marked decrease of 50% in safety incidents was observed, reflecting the profound impact of proactive strategies on enhancing safety protocols. However, these improvements are juxtaposed with an initial investment surge, where staff training costs increased by 30%, and expenditure on predictive maintenance tools rose by 25%. This research underscores the critical importance of a comprehensive and quantified understanding of maintenance strategies and their broader impacts on power plant performance. The study illustrates that while proactive maintenance demands initial investments, the long-term benefits significantly outweigh these costs, leading to enhanced operational efficiency, safety, and cost-effectiveness. The insights gleaned from this study provide invaluable guidance for power plant operators, stakeholders, and policymakers in their pursuit to optimize operations, improve safety standards, and achieve economic efficiencies, thereby advocating for a strategic shift towards more proactive maintenance approaches in power plant operations.

Extended ADM1 model to study trace metal speciation and its effects on anaerobic digestion

Trace metals (TM) are often added to anaerobic digestors (AD) to improve digestor performance and methane yield. Though numerous experimental studies are done to study TM effects, a model based on ADM1 will help plant operators in case of metal deprivation as there are analytical limitations in quantifying metal speciation which controls TM effects on AD. This study developed a complete dynamic model based on ADM1 with least and unavoidable model input information to study TM speciation and its influence on AD. In addition to the biochemical and gas stripping processes described in ADM1, the model introduced new physicochemical processes with effects of operational conditions like pH, temperature and ionic strength. The biogeochemical processes influencing TM speciation include microbial uptake, precipitation (sulphides, phosphates, carbonates), adsorption (onto biomass, soluble inerts and precipitates), inorganic complexation and organic complexation (with EDTA, VFAs, amino acids (AAs)). The model was tested under different scenarios: variable EDTA concentration and dosing form, variable substrate and variable ionic strength. Complexation of TMs with EDTA and AAs influences metal bioavailability and methane yield. Supplementing EDTA reduces TM dosing and dosing EDTA as metal-EDTA complex is more effective than supplementing metals and EDTA separately. TM-AA complexation depends on substrates as amino acid compounds vary with substrates. Ionic strength influences TM adsorption and precipitation processes and hence is an important operational parameter controlling the system like pH and temperature. The model has been compared to experimental data obtained from this study and has been successfully applied to predict cobalt deprivation in an anaerobic digestor.

Multi-time scale scheduling for virtual power plants: Integrating the flexibility of power generation and multi-user loads while considering the capacity degradation of energy storage systems

With the high proportion of renewable energy connected to the grid, the problem of insufficient flexibility in the power system has emerged. Renewable energy and controllable distributed resources can be aggregated and managed through virtual power plants, reducing the need for flexibility to a certain extent. Although building new energy storage systems can compensate for the lack of flexibility, it requires high initial investment costs. To address this, this paper proposes a lease mechanism for coal-fired units based on the combination of carbon credits and prices, providing the right to use coal-fired units for virtual power plants. Subsequently, different demand response strategies are utilized to control the controllable loads of various users, thereby providing certain controllable resources for the virtual power plant. Additionally, to ensure the optimal decision-making of the virtual power plant operator, a cost model accurately describing the capacity degradation state of the energy storage system is adopted. Moreover, a multi-time scale scheduling strategy of virtual power plant is implemented to fully utilize controllable resources of different time scales, effectively dealing with the power imbalance caused by multiple uncertainties. The results show that utilizing the leasing mechanism of coal-fired units and employing demand response strategies from different users can provide flexibility to the virtual power plant. The accuracy of the capacity degradation model used by the operator significantly impacts the optimality of the scheduling plan.

Methodology to Assist in Training Maneuvers of Operation of Pumps of a Nuclear Plant Using Virtual Reality

In power plants, one of the important maneuvers is the switching of pumps, where incorrect execution can even cause the unit to shut down. Therefore, the objective of this paper is to create a virtual environment for training the critical maneuver of the pump switching operation. The method used as reference was the specific procedure for the actual execution of the pump switching maneuver. Then the procedure was interpreted in order to reproduce in the virtual environment the same behaviors as in the real environment. Subsequently, visits to the field, control room, and control room simulator were undertaken in order to obtain material and data for the assembly of the virtual environment. Finally, with all the information collected, a virtual environment was created. The result was the creation of a system and equipment of interest for switching pumps interactively in the virtual environment, with great fidelity to the real environment. Tests were done by nuclear power plant operators and instructors on this virtual environment. The test results of this group demonstrated that the virtual environment created is a good tool to significantly assist in this type of training, as it promotes the experience of imposed challenges, which are common to several other systems of a nuclear power plant. In this way, the choice of a system to be reproduced in a virtual environment that represents the challenge of training critical maneuvers that affect the reactor was fundamental.

Sensor Fish Deployments at the Xayaburi Hydropower Plant: Measurements and Simulations

Fish protection is a priority in regions with ongoing and planned development of hydropower production, like the Mekong River system. The evaluation of the effects of turbine passage on the survival of migratory fish is a primary task for informing hydropower plant operators and authorities about the environmental performance of plant operations. The present work characterizes low pressures and collision rates through the Kaplan-type runners of the Xayaburi hydropower station. Both an experimental method based on the deployment of Sensor Fish and a numerical strategy based on flow and passage simulations were implemented on the analysis of two release elevations at one operating point. Nadir pressures and pressure drops through the runner were very sensitive to release elevation, but collision rates on the runner were not. The latter showed a frequency of occurrence of 8.2–9.3%. Measured magnitudes validated the corresponding simulation outcomes in regard to the averaged magnitudes as well as to the variability. Central to this study is that simulations were conducted based on current industry practices for designing turbines. Therefore, the reported agreement helps turbine engineers gain certainty about the prediction power of flow and trajectory simulations for fish passage assessments. This can accelerate the development of environmentally enhanced technology with minimum impact on natural resources.

Evaluation of the operational performance of Karbala waste water treatment plant under variable flow using GPS-X model

Abstract As Karbala City is a religious tourism destination, millions of tourists visit the city annually, so there is a high fluctuation of flow in the wastewater between the plants during the year. The aim of this study is to evaluate the performance of the wastewater treatment plant (WWTP) in Karbala, Iraq, in removing pollutants for different flow rates in five scenarios using the GPS-X model. The most important phase in modeling, which greatly impacts simulation accuracy, is characterizing the influent composition to meet the mass balance. As a result, the influent wastewater was initially described and thoroughly examined. The model has been calibrated, followed by the collected data’s validation. The sensitivity of different stoichiometric and kinetic factors in the GPS-X was examined and screened to calibrate the model. To demonstrate the consistency between the simulated and measured data, the route mean square error was used in this instance. The result showed that the Karbala WWTP complies with Iraqi environmental regulations for water discharged to surface water or water for other uses and has an appropriate efficiency of wastewater treatment even if the flow entering the plant reaches the peak flow rate of 180,000/day. An improvement in orthophosphate removal efficiency was observed as the flow rate increased because of the contact time in the anaerobic basins, which gradually decreased as the flow increased until it reached the appropriate time for PO 4 3 − {\text{PO}}_{4}^{3-} removal. The outcomes of the present study provide an impression to the operators of the treatment plant of the impact of fluctuating flow on the treatment plant. The developed model can also be used for future studies.

A full-scale operational digital twin for a water resource recovery facility-A case study of Eindhoven Water Resource Recovery Facility.

Digital transformation for the water sector has gained momentum in recent years, and many water resource recovery facilities modelers have already started transitioning from developing traditional models to digital twin (DT) applications. DTs simulate the operation of treatment plants in near real time and provide a powerful tool to the operators and process engineers for real-time scenario analysis and calamity mitigation, online process optimization, predictive maintenance, model-based control, and so forth. So far, only a few mature examples of full-scale DT implementations can be found in the literature, which only address some of the key requirements of a DT. This paper presents the development of a full-scale operational DT for the Eindhoven water resource recovery facility in The Netherlands, which includes a fully automated data-pipeline combined with a detailed mechanistic full-plant process model and a user interface co-created with the plant's operators. The automated data preprocessing pipeline provides continuous access to validated data, an influent generator provides dynamic predictions of influent composition data and allows forecasting 48 h into the future, and an advanced compartmental model of the aeration and anoxic bioreactors ensures high predictive power. The DT runs near real-time simulations every 2h. Visualization and interaction with the DT is facilitated by the cloud-based TwinPlant technology, which was developed in close interaction with the plant's operators. A set of predefined handles are made available, allowing users to simulate hypothetical scenarios such as process and equipment failures and changes in controller settings. The combination of the advanced data pipeline and process model development used in the Eindhoven DT and the active involvement of the operators/process engineers/managers in the development process makes the twin a valuable asset for decision making with long-term reliability. PRACTITIONER POINTS: A full-scale digital twin (DT) has been developed for the Eindhoven WRRF. The Eindhoven DT includes an automated continuous data preprocessing and reconciliation pipeline. A full-plant mechanistic compartmental process model of the plant has been developed based on hydrodynamic studies. The interactive user interface of the Eindhoven DT allows operators to perform what-if scenarios on various operational settings and process inputs. Plant operators were actively involved in the DT development process to make a reliable and relevant tool with the expected added value.

Methodology for Determining the Parameters of a Mathematical Model of the Dynamics of the Psychophysiological State of a Metallurgical Equipment Operator

Introduction. Mathematical modelling is effective in the analysis of industrial safety at metallurgical plants, in particular for tracking problems of the man — machine system. To introduce the time factor, recurrence relations (in a discrete model) and differential relations (in a continuous model) are used. However, it is also necessary to solve the problem of linking the model parameters to the real conditions of the production environment and to the human factor.The aim of this study is to create a method for determining the parameters of simulation mathematical models of the dynamics of the operator’s psychophysiological indicators affecting the work.Materials and Methods. The operator’s psychophysiological state (PPS) was assessed by performance, fatigue levels, and error rate. The data were collected by the Digital Correction Task (DCT) test. Based on the obtained results, the experimental values of the operator’s PPS indicators, which were reduced to the normalized scale [0, 1], were calculated. These indicators for a particular respondent, the mathematical model and the developed algorithm were used to determine the numerical values of the model parameters. In order to interpret the indicators of performance, fatigue and error rate, we introduced scales with five gradations.Results. The use of the authors’ modified version of the mathematical model showed a significant improvement in its prognostic properties. Out of 10 participants the best result was shown by respondent no. 7, the worst result was shown by respondent no. 8. During the first working hour (from 9.00 to 10.00) their performance increased almost equally, from 0.5–0.55 to almost 0.6. Then the score of respondent no. 7 increased and remained well above the “good” level until the end of the day. The score of respondent no. 8 dropped and was below average from 14.00 to 15.00. The difference was largely determined by the operators’ chronotypes. Their chronophysiological characteristics also affected fatigue and error rate. The model’s quality varied for different participants in the experiments. In one case it was excellent (mean relative error ≤5%), in three cases it was good (≤10%) and in four it was satisfactory (≤15%).Discussion and Conclusion. The proposed approach allows us to obtain the dynamic profiles of psychophysiological characteristics for every individual, to assess their interrelationships and to perform a prediction on the basis of a modified mathematical model. However, in order to extend the functionality of the models to the real working conditions of the metallurgical plant operator, it is necessary to increase the sample size, reduce the discrete time step and conduct studies for different working conditions, considering technological, climatic, environmental, psychological and other factors.

A standardized protocol for assessing the performance of automatic detection systems used in onshore wind power plants to reduce avian mortality

While wind power plants are an important contribution to the production of renewable energy to limit climate change, collision mortality from turbines is a danger for birds, including many protected species. To try to mitigate collision risks, automatic detection systems (ADSs) can be deployed on wind power plants; these work by detecting incoming birds using a detection/classification process and triggering a specific reaction (scaring off the bird or shutting down the turbine). Nonetheless, bird fatalities still occur at ADS-equipped wind power plants, which raises the question of the performance of these tools. To date, the lack of a transparent, peer-reviewed experimental process to compare the performance of types of ADS has meant there is no robust protocol to assess these systems.With the aim of filling this gap, we developed two standardized protocols that provide objective and unbiased assessments of the performance of different types of ADS, based on their probability of detecting/classifying birds at risk of collision. Both protocols rely on precise 3D tracking of wild birds by human observers using a laser rangefinder, and the comparison of these tracks with those detected and recorded by an ADS. The first protocol evaluates a system's general performance, generating comparable data for all types of ADS. In this protocol, detection/classification probability is estimated conditional on several abiotic and biotic environmental factors such as bird size, distance from the target, the flight angle and azimuth of the bird, as well as weather conditions. The second protocol aims to verify that the performance of an ADS installed on a given wind power plant complies with its regulatory requirements. In this protocol, detection/classification probability is specifically estimated for a given target species at a given regulatory detection distance. This protocol also estimates the proportion of time an ADS is functional on site over a year, and the proportion of reaction orders successfully operated by wind turbines. These protocols have been field-tested and made publicly available for use by government agencies and wind power plant operators.

Assessment of routine operation and maintenance work of a small community water treatment plant in North Guwahati, Assam, India

A water treatment plant's efficiency is reliant on its regular maintenance and operation. Many water treatment plants across the world are outsourced to private enterprises for day to day operation and maintenance. However, the impact of outsourcing may not always be profitable as design of faulty treatment units or carrying out improper routine operation and maintenance works by the private enterprise may ultimately affect the quality of potable water produced. The current study aims to evaluate the routine operation and maintenance tasks performed by an outsourced private enterprise at a small community water treatment facility in Assam. The performance monitoring of the treatment plant indicates that the plant is unable to produce potable water meeting national water quality standards. The turbidity of treated water is not able to meet the BIS acceptable limit of 1 NTU but it complies with the BIS permissible limit of 5 NTU. In general, the plant operators lack the skills required to operate the treatment plant effectively. Coagulant dosing is completely based on personal experience of plant operators instead of using jar test results. The backwashing of filter beds is carried out arbitrarily without considering the turbidity of filtered water. These factors have resulted in malfunctioning of the treatment units ultimately affecting the quality of potable water produced from the plant. The skill set of plant operators and chemist has been enhanced with bilingual stepwise instruction charts and on-hand training at the site. This aids in raising the standard of produced potable water. The turbidity of treated water is observed to be close to the BIS acceptable limit of 1 NTU.

Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: a review.

Biogas plant operators often face huge challenges in the monitoring, controlling and optimisation of the anaerobic digestion (AD) process, as it is very sensitive to surrounding changes, which often leads to process failure and adversely affects biogas production. Conventional implemented methods and mechanistic models are impractical and find it difficult to model the nonlinear and intricate interactions of the AD process. Thus, the development of machine learning (ML) algorithms has attracted considerable interest in the areas of process optimization, real-time monitoring, perturbation detection and parameter prediction. This paper provides a comprehensive and up-to-date overview of different machine learning algorithms, including artificial neural network (ANN), fuzzy logic (FL), adaptive network-based fuzzy inference system (ANFIS), support vector machine (SVM), genetic algorithm (GA) and particle swarm optimization (PSO) in terms of working mechanism, structure, advantages and disadvantages, as well as their prediction performances in modelling the biogas production. A few recent case studies of their applications and limitations are also critically reviewed and compared, providing useful information and recommendation in the selection and application of different ML algorithms. This review shows that the prediction efficiency of different ML algorithms is greatly impacted by variations in the reactor configurations, operating conditions, influent characteristics, selection of input parameters and network architectures. It is recommended to incorporate mixed liquor volatile suspended solids (MLVSS) concentration of the anaerobic digester (ranging from 16,500 to 46,700mg/L) as one of the input parameters to improve the prediction efficiency of ML modelling. This review also shows that the combination of different ML algorithms (i.e. hybrid GA-ANN model) could yield better accuracy with higher R2 (0.9986) than conventional algorithms and could improve the optimization model of AD. Besides, future works could be focused on the incorporation of an integrated digital twin system coupled with ML techniques into the existing Supervisory Control and Data Acquisition (SCADA) system of any biogas plant to detect any operational abnormalities and prevent digester upsets.

Research on Nuclear Power Plant Operational Task Complexity Assessment Method Based on Group Decision Making and Deep Learning of EEG Signals

This study aims to explore the correlation between the operational task complexity of nuclear power plant (NPP) operators and electroencephalogram (EEG) features. Initially, we segmented EEG signals according to operational steps and extracted a total of 120 time domain, frequency domain, and time-frequency domain features. Subsequently, we applied an adaptive principal component analysis (PCA) dimensionality reduction method to process the features. On the other hand, three experts were invited to evaluate the complexity of the operational tasks, and their evaluation data were synthesized using a group decision-making approach. A correlation analysis was performed between these data and the PCA-reduced feature data, identifying the features with the highest correlation coefficient for each participant. Then we built a long short-term memory model with the data of the first group of participants to predict the task complexity value and tested it with the data of the second group of participants. Testing the model with data from the second group yielded favorable results, with a training set mean squared error (MSE) of 0.025 and a testing set MSE of 0.078. The results of this study indicate a significant correlation between specific EEG features and task complexity in the operational tasks of NPP operators. The model established through a combination of group decision making and machine learning methods effectively predicted the task complexity levels for operators in different operational tasks. This research provides a new perspective on NPP operators’ cognitive load and operational tasks, holding practical significance for operator training and workload management.

Comparison of energy and environmental performance between warm and hot mix asphalt concrete production: A case study

The use of additives to produce warm bituminous mixtures in asphalt pavements gives the possibility to decrease temperatures with positive implications on energy consumption, and on emissions of greenhouse gases and airborne pollutants. This study investigates the changes in energy and environmental performance of an asphalt plant switching from hot to warm asphalt concrete production. A full-scale trial section was constructed in an Italian motorway with recycled bituminous mixtures containing SBS polymer modified bitumen. Reference hot mix asphalts (HMAs) mixed at 170 °C were compared with warm mix asphalts (WMAs) mixed at 130 °C with different chemical additives. Calculations and analysis were performed considering three production phases during which the production technologies (HMA and WMA) and the mixture types varied in temperatures, mixing duration, and quantity of virgin materials employed. Energy performance was calculated through values provided by the asphalt plant operator and thermodynamic equations. Emissions of CO2 were calculated based on energy consumption and emission factors reported in the literature for the Italian energy mix. Airborne pollutants were measured at the stack of the dryer drum. The results showed that for each mixture type, a reduction of 40 °C in the production temperature corresponds to 15% lower thermal energy values for the drying/heating of the aggregates, with consequent lower consumption of fuel oil. The drying/heating of aggregates for WMA lead to lower emissions of particulate matter, NOx, and VOCs compared to HMA.

Economic Feasibility of Reverse Osmosis (RO) Water Treatment Plants: A Case Study from Dimbulagala, Polonnaruwa, Sri Lanka

Social wellbeing is strongly linked with economic feasibility when it needs to attain with the technological advancements. Installation of Reverse Osmosis (RO) water treatment plants have been considered as a promising solution to provide clean water for human consumption, especially in the areas where the CKDu (chronic kidney disease of unknown etiology) prevails. At present, over 2,000 RO treatment plants were installed in the dry zone of Sri Lanka, yet economic feasibility for operation and maintenance of RO plants has not been assessed so far. The present study was intended to identify economic feasibility of operation and maintenance of RO plants. Investigations were carried out in six community RO plants which provided drinking water for over 17,000 people which accounts for 20% of total population in Dimbulagala Divisional Secretariat over a period of 12 months. Six in-depth interviews and questionnaire survey were caried out with RO plant operators. The operational cost per production of cubic meter of filtered water was computed by considering electricity consumption bills. The maintenance costs and service charges were also obtained from the records available with RO plant operators. The results found that the average electricity consumption to produce 1 cube of filtered water is approximately 9kWh and cost is LKR 734 (based on 2021 rates). The average water selling price ranged between LKR 1.00-2.50 per liter. The average monthly income generation from one RO plant is approximately between LKR 561- 875 per cube of filtered water and it largely depended on the type of water source, climatic conditions. It was found that the income generated from RO plants was sufficient enough to cover the operation cost (monthly electricity bill) and for the subsequent maintenance and service charges whenever required. It can be concluded that the use of RO treated water is an economically viable option to provide portable drinking water. 
 Keywords: CKDu, Economic sustainability, Drinking water, Purification cost

Enhancing the Performance of a Hazardous Waste Incineration Facility through the Usage of a Dedicated Application

Thermal transformation of waste carried out in a rotary kiln is a complex process, usually involving hazardous waste. Due to the great diversity of these wastes and their specific properties, the process of their thermal transformation may be associated with various types of operational problems. The reasons for their occurrence include, among others, processes potentially affecting the deterioration of the condition of the rotary kiln lining. In order to minimize problems, a tool is proposed to support the work of incineration plant operators. This is an application that enables generation of input material portions in subsequent kiln feeds. It is characterized by wide functionality, including the ability to assign higher weights to selected parameters. The application is based on an algorithm that takes into account the key waste parameters from the point of view of the thermal process, which have been given specific value ranges. Two series of simulations were performed with the same assumptions except for change in the weight for one of the parameters in the second case. In the first series, the following ranges of the considered parameters were obtained: calorific value 14.96–20.66 MJ/kg, pH 5.59–8.11, content of alkaline salts 1.42–7.39, content of chlorine 1.1–3.83, content of halogens 0.08–0.97. In the second series of simulations, the favored parameter was the calorific value, which ranged from 17.08 MJ/kg to 18.69 MJ/kg. The range of values for the remaining parameters changed slightly, with all meeting the criteria. Application tests showed the generation of waste mixtures with parameters consistent with the established ranges.

A Method for Projecting Cloud Shadows Onto a Central Receiver Field to Predict Receiver Damage

This work demonstrates methods of mapping high-spatial-resolution direct normal irradiance (DNI) data from satellites, Total Sky Imagers (TSIs), and analogous data sources onto a heliostat field for characterizing the spatial and temporal variation of the incident flux on a central receiver tower during cloud transient events. The mapping methods are incorporated into an optical software module that interfaces with CoPylot–SolarPILOT’s python API– to provide computationally efficient optical simulation of the heliostat field and the solar power tower. Eventually, this optical model will be incorporated into optimization models whereby a plant operator can understand the effects of cloud transient events on overall power production and receiver lifetime due to creep-fatigue damage and therefore make better informed decisions about receiver shutdown events. By more accurately modelling the effects of cloud events on receiver flux maps, this work may determine the magnitude and frequency of thermal cycling on receiver tubes and panels using actual or realistic cloud shapes instead of averaged DNI values–which may undercount the total cycle number. This work may also prevent unnecessary plant shutdowns due to overly precautionary control strategies and characterize the relative impact of various cloud types on receiver life. We plan to eventually integrate this methodology into the System Advisor Model (SAM) to improve performance model accuracy during periods of cloudiness. In this paper, we demonstrate generating DNI maps and mapping them to a solar field in CoPylot using 10 m resolution data from publicly available Sentinel-2 satellite data over the Crescent Dunes plant.

Enhanced Deep Predictive Modeling of Wastewater Plants With Limited Data

Deep Learning is being widely utilized in industrial process monitoring, control, and optimization. However, in the wastewater industry, its applications are still under-explored. This is because deep learning requires a large amount of labeled training data to induce effective predictive models. Due to the high cost of sensors and frequency and delay in sampling and laboratory analytics, wastewater treatment process data can be sparse with varying frequencies. One option to address training data limitations is to use transfer learning. However, due to the large covariate shift between the commonly adopted source domains for transfer learning, and the target domain of wastewater processes, this approach leads to unacceptable performance. We address this issue by proposing a novel synthetic data generation method for deep predictive modeling of wastewater plants. Employing a Markov process that utilizes random walk, our technique enables the generation of abundant annotated data for our target domain. The method preserves the temporal dynamics and distribution of the original data, thereby closely mimicking the potential original samples of the domain. We extensively evaluate our method over two different high rate algae based treatment data sets, demonstrating considerable performance gains over existing transfer learning. Our proposed algorithm can assist plant operators to deploy responsive supportive models with limited data.

Effect of regenerative effectiveness on the performance of simple gas turbine

The deregulation of the power and energy sector introduced a strong element of competition. Power plant operators try to develop techniques to maximize profits in the dynamic power industry. New methods of improving and optimizing simple gas turbine power plants are needed to enhance operational decision making and therefore to maximize power plant profitability by reducing operational and maintenance cost and increasing revenue. In this work, the thermodynamic model equations of both a simple gas turbine and regenerative gas turbine cycle were used to analyses the effect of increase in the regenerative effectiveness to the performance of the simple gas turbine. Microsoft Excel 2010 software was used to carried out the analysis. It was observed that for the simple gas turbine the thermal efficiency, heat rate, specific fuel consumption and heat addition have a constant value of 35.4%, 9630Btu/Kwh, 0.2768kg/Kwh, 44237.8Kw, all through, while for the regenerative gas turbine cycle as the value of the regenerative effectiveness increases from 0.8 to 1.0, the thermal efficiency increases from 41.1% to 65.0%, the heat rate decreases from 8753Btu/Kwh to 5536 Btu/Kwh , specific fuel consumption decreases from 0.248945122Kg/Kw.h to 0.157432413 Kg/Kw.h and heat addition reduces from 39782.5Kw to 25158.4Kw. It was seen clearly that the regenerative gas turbine cycle has higher thermal efficiency compare to the simple gas turbine, and as the regenerative effectiveness of the regenerative gas turbine cycle increases the performance of the regenerative gas turbine increases.

Foam Segmentation in Wastewater Treatment Plants

The excessive accumulation of foam in wastewater treatment plant (WWTP) tanks can impede proper aeration, hindering the effective removal of organic matter from the water. This study proposes a novel technique to monitor in real time the presence of foams in WWTP tanks by using texture segmentation models trained with centralized and federated approaches. These models are designed to segment the foam and quantify the percentage of foam coverage across the entire tank surface. This data provides plant operators with crucial information for identifying the optimal time for foam removal. The proposed methodology is integrated into an image processing pipeline that involves acquiring images using a PTZ camera, ensuring the absence of anomalies in the captured images, and implementing a real-time communication method for event notifications to plant operators. The models exhibit noteworthy performance, achieving an 86% Dice score in foam segmentation, with comparable results obtained through both centralized and federated training. Implemented in a wastewater treatment plant, this integrated pipeline enhances operational efficiency while concurrently reducing costs.

Unexpected tax refunds and capital market efficiency: Evidence from the German nuclear fuel tax

We investigate the efficiency of capital markets in reacting to a ruling of the German Constitutional Court in 2017, which led to a large unexpected tax refund for the three German-based operators of nuclear power plants. Unlike events used by prior research, this unique firm-specific event allows us to assess both, the magnitude and speed of the market reaction. With regard to magnitude, we find that the tax refund is fully impounded into stock prices within a few trading days after the court ruling. When investigating the speed of the market reaction more granularly, we show that 63% to 82% of the entire abnormal returns of the event date are already realized within the first minute after the announcement of the court ruling and, moreover, that significant market reactions occur even within 5 to 15 s.