Provision of secondary frequency regulation by coordinated dispatch of industrial loads and thermal power plants

Demand responsive industrial loads with high thermal inertia have potential to provide ancillary service for frequency regulation in the power market. To capture the benefit, this study proposes a new hierarchical framework to coordinate the demand responsive industrial loads with thermal power plants in an industrial park for secondary frequency control. In the proposed framework, demand responsive loads and generating resources are coordinated for optimal dispatch in two-time scales: (1) the regulation reserve of the industrial park is optimally scheduled in a day-ahead manner. The stochastic regulation signal is replaced by the specific extremely trajectories. Furthermore, the extremely trajectories are achieved by the day-ahead predicted regulation mileage. The resulting benefit is to transform the stochastic reserve scheduling problem into a deterministic optimization; (2) a model predictive control strategy is proposed to dispatch the industry park in real time with an objective to maximize the revenue. The proposed technology is tested using a real-world industrial electrolysis power system based upon Pennsylvania, Jersey, and Maryland (PJM) power market. Various scenarios are simulated to study the performance of the proposed approach to enable industry parks to provide ancillary service into the power market. The simulation results indicate that an industrial park with a capacity of 500 MW can provide up to 40 MW ancillary service for participation in the secondary frequency regulation. The proposed strategy is demonstrated to be capable of maintaining the economic and secure operation of the industrial park while satisfying performance requirements from the real world regulation market

AI based state observer for optimal process control: application to digital twins of manufacturing plants

Les plantes de fabricació estan subjectes a restriccions dinàmiques que requereixen una optimització robusta per millorar el rendiment i l' eficiència del sistema. En aquest projecte es presenta un nou sistema de control òptim basat en IA per a un bessó digital d' una planta de fabricació. El sistema proposat implementa un observador d' estat basat en IA per predir l' estat intern d' un model de procés altament incert i no lineal, tal com seria un sistema de producció real. Una funció d' optimització multi-objectiu es utilitzada per controlar els paràmetres de producció i mantenir el procés funcionant en condicions òptimes. El mètode d'Optimització del Control basat en AI es va implementar en un cas d'estudi d'una planta de fabricació d'acer. El rendiment del sistema es va avaluar utilitzant els KPIs de fabricació rellevants, com ara les taxes d'utilització i productivitat de l'equip del procés. L'ús de sistema de control optimitzat via AI millora amb èxit els KPIs de procés i potencialment podria reduir els costos de producció.Las plantas de fabricación están sujetas a restricciones dinámicas que requieren una optimización robusta para mejorar el rendimiento y la eficiencia. En este informe se presenta un nuevo sistema de control óptimo basado en IA para un gemelo digital de una planta de fabricación. El sistema propuesto implementa un observador de estado basado en IA para predecir el estado interno de un modelo de proceso altamente incierto y no lineal, tal y como sería un sistema de producción real. Una función de optimización multiobjetivo es utilizada para controlar los parámetros de producción y mantener el proceso funcionando en condiciones óptimas. El método de Optimización del Control basado en AI se implementó en un caso de estudio de una planta de fabricación de acero. El rendimiento del sistema se evaluó utilizando los KPIs de fabricación relevantes, como la utilización del equipo y las tasas de productividad del proceso. El uso del sistema de control óptimo de IA mejora los KPIs del proceso y podría reducir potencialmente los costos de producción.Manufacturing plants are subject to dynamic constrains requiring robust optimization methods for improved performance and efficiency. A novel AI based optimal control system for a Digital Twin of a manufacturing plant is presented in this report. The proposed system implements an AI based state observer to predict the internal state of a highly uncertain and non-linear process model, such as a real production system. A multi-objective optimization function is used to control production parameters and keeps the process running at an optimal condition. The AI Optimization Control method was implemented on a study case on a steel manufacturing plant. The performance of the system was evaluated using the relevant manufacturing KPIs such as the equipment utilization and productivity rates of the process. The use of the AI optimal control system successfully improves the process KPIs and could potentially reduce production costs

Modeling and Simulation of Metallurgical Processes in Ironmaking and Steelmaking

In recent years, improving the sustainability of the steel industry and reducing its CO2 emissions has become a global focus. To achieve this goal, further process optimization in terms of energy and resource efficiency and the development of new processes and process routes are necessary. Modeling and simulation have established themselves as invaluable sources of information for otherwise unknown process parameters and as an alternative to plant trials that involves lower costs, risks, and time. Models also open up new possibilities for model-based control of metallurgical processes. This Special Issue focuses on recent advances in the modeling and simulation of unit processes in iron and steelmaking. It includes reviews on the fundamentals of modeling and simulation of metallurgical processes, as well as contributions from the areas of iron reduction/ironmaking, steelmaking via the primary and secondary route, and continuous casting

Industrial time series modelling by means of the neo-fuzzy neuron

Abstract—Industrial process monitoring and modelling represents a critical step in order to achieve the paradigm of Zero Defect Manufacturing. The aim of this paper is to introduce the Neo-Fuzzy Neuron method to be applied in industrial time series modelling. Its open structure and input independency provides fast learning and convergence capabilities, while assuring a proper accuracy and generalization in the modelled output. First, the auxiliary signals in the database are analyzed in order to find correlations with the target signal. Second, the Neo-Fuzzy Neuron is configured and trained according by means of the auxiliary signal, past instants and dynamics information of the target signal. The proposed method is validated by means of real data from a Spanish copper rod industrial plant, in which a critical signal regarding copper refrigeration process is modelled. The obtained results indicate the suitability of the Neo-Fuzzy Neuron method for industrial process modelling.Postprint (published version

Power Quality

Electrical power is becoming one of the most dominant factors in our society. Power generation, transmission, distribution and usage are undergoing signifi cant changes that will aff ect the electrical quality and performance needs of our 21st century industry. One major aspect of electrical power is its quality and stability – or so called Power Quality. The view on Power Quality did change over the past few years. It seems that Power Quality is becoming a more important term in the academic world dealing with electrical power, and it is becoming more visible in all areas of commerce and industry, because of the ever increasing industry automation using sensitive electrical equipment on one hand and due to the dramatic change of our global electrical infrastructure on the other. For the past century, grid stability was maintained with a limited amount of major generators that have a large amount of rotational inertia. And the rate of change of phase angle is slow. Unfortunately, this does not work anymore with renewable energy sources adding their share to the grid like wind turbines or PV modules. Although the basic idea to use renewable energies is great and will be our path into the next century, it comes with a curse for the power grid as power fl ow stability will suff er. It is not only the source side that is about to change. We have also seen signifi cant changes on the load side as well. Industry is using machines and electrical products such as AC drives or PLCs that are sensitive to the slightest change of power quality, and we at home use more and more electrical products with switching power supplies or starting to plug in our electric cars to charge batt eries. In addition, many of us have begun installing our own distributed generation systems on our rooft ops using the latest solar panels. So we did look for a way to address this severe impact on our distribution network. To match supply and demand, we are about to create a new, intelligent and self-healing electric power infrastructure. The Smart Grid. The basic idea is to maintain the necessary balance between generators and loads on a grid. In other words, to make sure we have a good grid balance at all times. But the key question that you should ask yourself is: Does it also improve Power Quality? Probably not! Further on, the way how Power Quality is measured is going to be changed. Traditionally, each country had its own Power Quality standards and defi ned its own power quality instrument requirements. But more and more international harmonization efforts can be seen. Such as IEC 61000-4-30, which is an excellent standard that ensures that all compliant power quality instruments, regardless of manufacturer, will produce of measurement instruments so that they can also be used in volume applications and even directly embedded into sensitive loads. But work still has to be done. We still use Power Quality standards that have been writt en decades ago and don’t match today’s technology any more, such as fl icker standards that use parameters that have been defi ned by the behavior of 60-watt incandescent light bulbs, which are becoming extinct. Almost all experts are in agreement - although we will see an improvement in metering and control of the power fl ow, Power Quality will suff er. This book will give an overview of how power quality might impact our lives today and tomorrow, introduce new ways to monitor power quality and inform us about interesting possibilities to mitigate power quality problems. Regardless of any enhancements of the power grid, “Power Quality is just compatibility” like my good old friend and teacher Alex McEachern used to say. Power Quality will always remain an economic compromise between supply and load. The power available on the grid must be suffi ciently clean for the loads to operate correctly, and the loads must be suffi ciently strong to tolerate normal disturbances on the grid

Thermodynamic and parametric modeling in the refining of high carbon ferrochromium alloys using manually operated AODs

M.Sc. (50/50) Research project submitted to School of Chemical and Metallurgical Engineering, Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, South Africa July 2017This study and the work done involves investigating the effects of different parameters on the decarburization process of high carbon ferrochromium melts to produce medium carbon ferrochrome, and takes into account the manipulation of the different parameters and thermodynamic models based on actual plant data. Process plant data was collected from a typical plant producing medium carbon ferrochrome alloys using AODs. The molten alloy was tapped from the EAF and charged into the AOD for decarburization using oxygen and nitrogen gas mixtures. The gases were blown into the converter through the bottom tuyeres. Metal and slag samples and temperature measurements were taken throughout the duration of each heat. The decarburization process was split into two main intervals namely first stage blow (where carbon content in the metal bath is between 2-8 wt. % C) and second stage blow (carbon mass% below 2 wt. %). The first and second blow stages were differentiated by the gas flow rates whereby the first stage was signified by gas flow ratio of 2:1 (O2:N2), whilst the stage blow had 1:1 ratio of oxygen and nitrogen respectively. The effect of Cr mass% on carbon activity and how it relates to rate of decarburization was investigated, and the results indicated that an increase in Cr 66.54 – 70.5 wt. % reduced carbon activity in the metal bath from 0.336 – 0.511 for the first blowing stage. For the second blowing stage, the increase in Cr mass % of 67.22 – 71.65 wt. % resulted in an increase in C activity from 0.336 – 0.57. The trend showed that an increase in chromium composition resulted in a decrease in carbon activity and the same increase in Cr mass% resulted in reduced carbon solubility. Based on the plant data, it was observed that the rate of decarburization was time dependent, that is, the longer the decarburization time interval, the better the carbon removal from the metal bath. An interesting observation was that the change in carbon mass percent from the initial composition to the final (Δ%C) decreased from 10.18 – 8.37 wt. % with the increase in Cr/C ratio from 8.37 – 10.18. This effect was attributed to the chromium affinity for carbon and the fact that an increase in chromium content in the bath was seen to reduce activity of carbon. It was also observed that the effect of the Cr/C ratio was more significant in the first stage of the blowing process compared to the second blowing stage. A mass and energy balance model was constructed for the process under study to predict composition of the metal bath at any time interval under specified plant conditions and parameters. The model was used to predict the outcome of the process by manipulating certain parameters to achieve a set target. By keeping the gas flow rates, blowing times, gas ratios and initial metal bath temperature unchanged, the effect of initial temperature on decarburization in the converter was investigated. The results showed that the carbon end point with these parameters fixed decreased with increasing initial temperature, and this was supported by literature. The partial pressure of oxygen was observed to increase with decrease in C mass % between the first and second blow stages. For the second stage blow the partial pressure changed from 5.52*10-12 – 2.1*10-10 and carbon mass % increased from 0.754 – 2.99 wt. %. A carbon mass % of 7.87 had an oxygen partial pressure of 4.51*10-13 whilst a lower carbon content of 1.53 wt. % had an oxygen partial pressure of 8.06*10-11. The CO partial pressure however increased with increase in carbon composition in the metal bath. When the oxygen flow rate increased, a corresponding increase in the carbon removed (Δ%C) was observed. For the first stage of the blowing process, an increase in oxygen flow rate from 388.67 – 666.5Nm3 resulted in an increase in carbon removed from 5.06 – 7.28 wt. %. The second blowing stage had lower oxygen flow rates because of the carbon levels remaining in the metal bath were around +/- 2 wt. %. In this stage oxygen flow rates increased from 125 – 286.67 Nm3 and carbon removed (Δ%C) from 0.16 – 2.093 wt. %. The slag showed that an increase in basicity resulted in an increase in Cr2O3 in the slag. As the basicity increased from 0.478 – 1.281, this resulted in an increase in Cr2O3 increase from 0.26 – 0.68. Nitrogen solubility in the metal bath was investigated and it was observed that it increased with increasing Cr mass %. The increase in nitrogen solubility with increasing Cr mass % was independent of the nitrogen partial pressures.MT201

Index to 1981 NASA Tech Briefs, volume 6, numbers 1-4

Short announcements of new technology derived from the R&D activities of NASA are presented. These briefs emphasize information considered likely to be transferrable across industrial, regional, or disciplinary lines and are issued to encourage commercial application. This index for 1981 Tech Briefs contains abstracts and four indexes: subject, personal author, originating center, and Tech Brief Number. The following areas are covered: electronic components and circuits, electronic systems, physical sciences, materials, life sciences, mechanics, machinery, fabrication technology, and mathematics and information sciences

Water Detection Framework for Industrial Electric Arc Furnaces

This thesis develops a framework for water detection in an industrial electric arc furnace. The objective of the framework is to prevent water leak furnace explosions. This framework consists of a hybrid algorithm and a fault detection method. The hybrid algorithm consists of a mechanistic model and an empirical model. The hybrid algorithm and the fault detection method developed in this work are implemented on two industrial AC electric arc furnaces. The names of the plants and details of the operations were withheld for confidentiality reasons. The first problem treated in this work was collecting the required data. The data required for this work included EAF operational data and off-gas composition. Both melt-shops did not have off-gas analysis systems and hence an off-gas analyzer with an HMI/SCADA data collection system was installed for each furnace. EAF operational data was sent to the data HMI/SCADA collection system installed at each melt-shop. The off-gas compositions measured in both melt-shops were CO, CO2, O2, H2, N2, and H2O. Once all required data was collected then the framework to detect water was developed. In order to test the water detection framework developed in this work, industrial trials were completed where water was intentionally added into the furnace by increasing the electrode spray water flow rate. The mechanistic model is completed by performing a mass balance on the furnace. The model provides a boundary with upper and lower limits in real-time of the expected EAF off-gas water vapor leaving the furnace. The mechanistic model of the hybrid algorithm has shown in both industrial EAFs that it provides a valuable on-line monitoring tool to the operator on what boundary to expect for the off-gas water vapor. There are many input variables and historical heats in an EAF operation; hence before building the empirical predictive component of the hybrid algorithm, heats selection model and input variables selection model are constructed based on latent variable methods. The outcome of the heats selection model is heats with normal operation. The outcome of the input variables selection model is variables that are highly correlated with the off-gas water vapor. Once the heats and the input variables are selected, then the empirical predictive models are developed. Empirical predictive models investigated in this work are: statistical fingerprinting, artificial neural network, and multiway projection to latent structures. Robustness issues with each method are discussed and a performance comparison between the methods is presented. The last section of this thesis proposes a novel approach to detecting water leaks in the furnace

Challenges and Prospects of Steelmaking Towards the Year 2050

The world steel industry is strongly based on coal/coke in ironmaking, resulting in huge carbon dioxide emissions corresponding to approximately 7% of the total anthropogenic CO2 emissions. As the world is experiencing a period of imminent threat owing to climate change, the steel industry is also facing a tremendous challenge in next decades. This themed issue makes a survey on the current situation of steel production, energy consumption, and CO2 emissions, as well as cross-sections of the potential methods to decrease CO2 emissions in current processes via improved energy and materials efficiency, increasing recycling, utilizing alternative energy sources, and adopting CO2 capture and storage. The current state, problems and plans in the two biggest steel producing countries, China and India are introduced. Generally contemplating, incremental improvements in current processes play a key role in rapid mitigation of specific emissions, but finally they are insufficient when striving for carbon neutral production in the long run. Then hydrogen and electrification are the apparent solutions also to iron and steel production. The book gives a holistic overview of the current situation and challenges, and an inclusive compilation of the potential technologies and solutions for the global CO2 emissions problem

Métodos machine learning para la predicción de inclusiones no metálicas en alambres de acero para refuerzo de neumáticos

ABSTRACT: Non-metallic inclusions are unavoidably produced during steel casting resulting in lower mechanical strength and other detrimental effects. This study was aimed at developing a reliable Machine Learning algorithm to classify castings of steel for tire reinforcement depending on the number and properties of inclusions, experimentally determined. 855 observations were available for training, validation and testing the algorithms, obtained from the quality control of the steel. 140 parameters are monitored during fabrication, which are the features of the analysis; the output is 1 or 0 depending on whether the casting is rejected or not. The following algorithms have been employed: Logistic Regression, K-Nearest Neighbors, Support Vector Classifier (linear and RBF kernels), Random Forests, AdaBoost, Gradient Boosting and Artificial Neural Networks. The reduced value of the rejection rate implies that classification must be carried out on an imbalanced dataset. Resampling methods and specific scores for imbalanced datasets (Recall, Precision and AUC rather than Accuracy) were used. Random Forest was the most successful method providing an AUC in the test set of 0.85. No significant improvements were detected after resampling. The improvement derived from implementing this algorithm in the sampling procedure for quality control during steelmaking has been quantified. In this sense, it has been proved that this tool allows the samples with a higher probability of being rejected to be selected, thus improving the effectiveness of the quality control. In addition, the optimized Random Forest has enabled to identify the most important features, which have been satisfactorily interpreted on a metallurgical basis.RESUMEN: Las inclusiones no metálicas se producen inevitablemente durante la fabricación del acero, lo que resulta en una menor resistencia mecánica y otros efectos perjudiciales. El objetivo de este estudio fue desarrollar un algoritmo fiable para clasificar las coladas de acero de refuerzo de neumáticos en función del número y el tipo de las inclusiones, determinadas experimentalmente. Se dispuso de 855 observaciones para el entrenamiento, validación y test de los algoritmos, obtenidos a partir del control de calidad del acero. Durante la fabricación se controlan 140 parámetros, que son las características del análisis; el resultado es 1 ó 0 dependiendo de si la colada es rechazada o no. Se han empleado los siguientes algoritmos: Regresión Logística, Vecinos K-Cercanos, Clasificador de Vectores Soporte (kernels lineales y RBF), Bosques Aleatorios, AdaBoost, Gradient Boosting y Redes Neurales Artificiales. El bajo índice de rechazo implica que la clasificación debe llevarse a cabo en un set de datos desequilibrado. Se utilizaron métodos de remuestreo y métricas específicas para conjuntos de datos desequilibrados (Recall, Precision y AUC en lugar de Accuracy). Random Forest fue el algoritmo más exitoso que proporcionó un AUC en los datos de test de 0.83. No se detectaron mejoras significativas después del remuestreo. Se ha cuantificado la mejora derivada de la implementación de este algoritmo en el procedimiento de muestreo para el control de calidad durante la fabricación de acero. En este sentido, se ha comprobado que esta herramienta permite seleccionar las muestras con mayor probabilidad de ser rechazadas, mejorando así la eficacia del control de calidad. Además, el Random Forest optimizado ha permitido identificar las variables más importantes, que han sido interpretadas satisfactoriamente sobre una base metalúrgica.Máster en Ciencia de Dato