Production Optimization Indexed to the Market Demand Through Neural Networks

Connectivity, mobility and real-time data analytics are the prerequisites for a new model of intelligent production management that facilitates communication between machines, people and processes and uses technology as the main driver. Many works in the literature treat maintenance and production management in separate approaches, but there is a link between these areas, with maintenance and its actions aimed at ensuring the smooth operation of equipment to avoid unnecessary downtime in production. With the advent of technology, companies are rushing to solve their problems by resorting to technologies in order to fit into the most advanced technological concepts, such as industries 4.0 and 5.0, which are based on the principle of process automation. This approach brings together database technologies, making it possible to monitor the operation of equipment and have the opportunity to study patterns of data behavior that can alert us to possible failures. The present thesis intends to forecast the pulp production indexed to the stock market value.The forecast will be made by means of the pulp production variables of the presses and the stock exchange variables supported by artificial intelligence (AI) technologies, aiming to achieve an effective planning. To support the decision of efficient production management, in this thesis algorithms were developed and validated with from five pulp presses, as well as data from other sources, such as steel production and stock exchange, which were relevant to validate the robustness of the model. This thesis demonstrated the importance of data processing methods and that they have great relevance in the model input since they facilitate the process of training and testing the models. The chosen technologies demonstrated good efficiency and versatility in performing the prediction of the values of the variables of the equipment, also demonstrating robustness and optimization in computational processing. The thesis also presents proposals for future developments, namely in further exploration of these technologies, so that there are market variables that can calibrate production through forecasts supported on these same variables.Conectividade, mobilidade e análise de dados em tempo real são pré-requisitos para um novo modelo de gestão inteligente da produção que facilita a comunicação entre máquinas, pessoas e processos, e usa a tecnologia como motor principal. Muitos trabalhos na literatura tratam a manutenção e a gestão da produção em abordagens separadas, mas existe uma correlação entre estas áreas, sendo que a manutenção e as suas políticas têm como premissa garantir o bom funcionamento dos equipamentos de modo a evitar paragens desnecessárias na linha de produção. Com o advento da tecnologia há uma corrida das empresas para solucionar os seus problemas recorrendo às tecnologias, visando a sua inserção nos conceitos tecnológicos, mais avançados, tais como as indústrias 4.0 e 5.0, as quais têm como princípio a automatização dos processos. Esta abordagem junta as tecnologias de sistema de informação, sendo possível fazer o acompanhamento do funcionamento dos equipamentos e ter a possibilidade de realizar o estudo de padrões de comportamento dos dados que nos possam alertar para possíveis falhas. A presente tese pretende prever a produção da pasta de papel indexada às bolsas de valores. A previsão será feita por via das variáveis da produção da pasta de papel das prensas e das variáveis da bolsa de valores suportadas em tecnologias de artificial intelligence (IA), tendo como objectivo conseguir um planeamento eficaz. Para suportar a decisão de uma gestão da produção eficiente, na presente tese foram desenvolvidos algoritmos, validados em dados de cinco prensas de pasta de papel, bem como dados de outras fontes, tais como, de Produção de Aço e de Bolsas de Valores, os quais se mostraram relevantes para a validação da robustez dos modelos. A presente tese demonstrou a importância dos métodos de tratamento de dados e que os mesmos têm uma grande relevância na entrada do modelo, visto que facilita o processo de treino e testes dos modelos. As tecnologias escolhidas demonstraram uma boa eficiência e versatilidade na realização da previsão dos valores das variáveis dos equipamentos, demonstrando ainda robustez e otimização no processamento computacional. A tese apresenta ainda propostas para futuros desenvolvimentos, designadamente na exploração mais aprofundada destas tecnologias, de modo a que haja variáveis de mercado que possam calibrar a produção através de previsões suportadas nestas mesmas variáveis

Utilizing decision forest regression machine learning algorithm to predict filling line utilization for optimal maintenance scheduling

CITATION: Smith, L., Zastron, M. & Vlok, P. J. 2018. Utilizing decision forest regression machine learning algorithm to predict filling line utilization for optimal maintenance scheduling. In SAIIE29 Proceedings, 24-26 October 2018, Spier, Stellenbosch, South Africa.The original publication is available at https://conferences.sun.ac.za/index.php/saiie29/saiie29/schedConf/presentationsSmall margins within the packaging industry mean financial success in this field relies on high equipment availability. To achieve this high equipment availability, maintenance schedules should be carefully planned to minimize downtime. A key component of maintenance schedule planning is predicting equipment utilization. This can prove very difficult as there are many variables such as market demand, seasonality of products, capability and diversity of equipment, and inherent reliability, to name a few. Even some of the leading players in the packaging industry treat the complexities and chaos involved with predicting equipment utilization as a topic best avoided. Current approaches to this problem range from no prediction at all to only a simple linear extrapolation. This paper investigates the merits of using machine learning algorithms to predict equipment utilization in the packaging industry with the aim of optimizing maintenance schedules. Machine learning entails pattern recognition of past data and inclusion of pertinent variables in the present to forecast behaviour. This paper begins with a brief literature review of the field before using data, obtained from a multinational packaging company, to test some of the most promising methods of machine learning in a case study.https://conferences.sun.ac.za/index.php/saiie29/saiie29/paper/view/3552Publisher's versio

Machine Learning-Based Analysis of a Wind Turbine Manufacturing Operation: A Case Study

This study analyzes the lead time of the bending operation in the wind turbine tower manufacturing process. Since the operation involves a significant amount of employee interaction and the parts processed are heavy and voluminous, there is considerable variability in the recorded lead times. Therefore, a machine learning regression analysis has been applied to the bending process. Two machine learning algorithms have been used: a multivariate Linear Regression and the M5P method. The goal of the analysis is to gain a better understanding of the effect of several factors (technical, organizational, and experience-related) on the bending process times, and to attempt to predict these operation times as a way to increase the planning and controlling capacity of the plant. The inclusion of the experience-related variables serves as a basis for analyzing the impact of age and experience on the time-wise efficiency of workers. The proposed approach has been applied to the case of a Spanish wind turbine tower manufacturer, using data from the operation of its plant gathered between 2018 and 2021. The results show that the trained models have a moderate predictive power. Additionally, as shown by the output of the regression analysis, there are variables that would presumably have a significant impact on lead times that have been found to be non-factors, as well as some variables that generate an unexpected degree of variability

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Overløpskontroll i avløpsnett med forskjellige modelleringsteknikker og internet of things

Increased urbanization and extreme rainfall events are causing more frequent instances of sewer overflow, leading to the pollution of water resources and negative environmental, health, and fiscal impacts. At the same time, the treatment capacity of wastewater treatment plants is seriously affected. The main aim of this Ph.D. thesis is to use the Internet of Things and various modeling techniques to investigate the use of real-time control on existing sewer systems to mitigate overflow. The role of the Internet of Things is to provide continuous monitoring and real-time control of sewer systems. Data collected by the Internet of Things are also useful for model development and calibration. Models are useful for various purposes in real-time control, and they can be distinguished as those suitable for simulation and those suitable for prediction. Models that are suitable for a simulation, which describes the important phenomena of a system in a deterministic way, are useful for developing and analyzing different control strategies. Meanwhile, models suitable for prediction are usually employed to predict future system states. They use measurement information about the system and must have a high computational speed. To demonstrate how real-time control can be used to manage sewer systems, a case study was conducted for this thesis in Drammen, Norway. In this study, a hydraulic model was used as a model suitable for simulation to test the feasibility of different control strategies. Considering the recent advances in artificial intelligence and the large amount of data collected through the Internet of Things, the study also explored the possibility of using artificial intelligence as a model suitable for prediction. A summary of the results of this work is presented through five papers. Paper I demonstrates that one mainstream artificial intelligence technique, long short-term memory, can precisely predict the time series data from the Internet of Things. Indeed, the Internet of Things and long short-term memory can be powerful tools for sewer system managers or engineers, who can take advantage of real-time data and predictions to improve decision-making. In Paper II, a hydraulic model and artificial intelligence are used to investigate an optimal in-line storage control strategy that uses the temporal storage volumes in pipes to reduce overflow. Simulation results indicate that during heavy rainfall events, the response behavior of the sewer system differs with respect to location. Overflows at a wastewater treatment plant under different control scenarios were simulated and compared. The results from the hydraulic model show that overflows were reduced dramatically through the intentional control of pipes with in-line storage capacity. To determine available in-line storage capacity, recurrent neural networks were employed to predict the upcoming flow coming into the pipes that were to be controlled. Paper III and Paper IV describe a novel inter-catchment wastewater transfer solution. The inter-catchment wastewater transfer method aims at redistributing spatially mismatched sewer flows by transferring wastewater from a wastewater treatment plant to its neighboring catchment. In Paper III, the hydraulic behaviors of the sewer system under different control scenarios are assessed using the hydraulic model. Based on the simulations, inter-catchment wastewater transfer could efficiently reduce total overflow from a sewer system and wastewater treatment plant. Artificial intelligence was used to predict inflow to the wastewater treatment plant to improve inter-catchment wastewater transfer functioning. The results from Paper IV indicate that inter-catchment wastewater transfer might result in an extra burden for a pump station. To enhance the operation of the pump station, long short-term memory was employed to provide multi-step-ahead water level predictions. Paper V proposes a DeepCSO model based on large and high-resolution sensors and multi-task learning techniques. Experiments demonstrated that the multi-task approach is generally better than single-task approaches. Furthermore, the gated recurrent unit and long short-term memory-based multi-task learning models are especially suitable for capturing the temporal and spatial evolution of combined sewer overflow events and are superior to other methods. The DeepCSO model could help guide the real-time operation of sewer systems at a citywide level.publishedVersio

Electrical power prediction through a combination of multilayer perceptron with water cycle ant lion and satin bowerbird searching optimizers

Predicting the electrical power (PE) output is a significant step toward the sustainable development of combined cycle power plants. Due to the effect of several parameters on the simulation of PE, utilizing a robust method is of high importance. Hence, in this study, a potent metaheuristic strategy, namely, the water cycle algorithm (WCA), is employed to solve this issue. First, a nonlinear neural network framework is formed to link the PE with influential parameters. Then, the network is optimized by the WCA algorithm. A publicly available dataset is used to feed the hybrid model. Since the WCA is a population-based technique, its sensitivity to the population size is assessed by a trial-and-error effort to attain the most suitable configuration. The results in the training phase showed that the proposed WCA can find an optimal solution for capturing the relationship between the PE and influential factors with less than 1% error. Likewise, examining the test results revealed that this model can forecast the PE with high accuracy. Moreover, a comparison with two powerful benchmark techniques, namely, ant lion optimization and a satin bowerbird optimizer, pointed to the WCA as a more accurate technique for the sustainable design of the intended system. Lastly, two potential predictive formulas, based on the most efficient WCAs, are extracted and presented

Machine Learning Tool for Transmission Capacity Forecasting of Overhead Lines based on Distributed Weather Data

Die Erhöhung des Anteils intermittierender erneuerbarer Energiequellen im elektrischen Energiesystem ist eine Herausforderung für die Netzbetreiber. Ein Beispiel ist die Zunahme der Nord-Süd Übertragung von Windenergie in Deutschland, die zu einer Erhöhung der Engpässe in den Freileitungen führt und sich direkt in den Stromkosten der Endverbraucher niederschlägt. Neben dem Ausbau neuer Freileitungen ist ein witterungsabhängiger Freileitungsbetrieb eine Lösung, um die aktuelle Auslastung des Systems zu verbessern. Aus der Analyse in einer Probeleitung in Deutschland wurde gezeigt, dass einen Zuwachs von ca. 28% der Stromtragfähigkeit eine Reduzierung der Kosten für Engpassmaßnahmen um ca. 55% bedeuten kann. Dieser Vorteil kann nur vom Netzbetreiber wahrgenommen werden, wenn eine Belastbarkeitsprognose für die Stromerzeugunsgplanung der konventionellen Kraftwerke zur Verfügung steht. Das in dieser Dissertation vorgestellte System prognostiziert die Belastbarkeit von Freileitungen für 48 Stunden, mit einer Verbesserung der Prognosegenauigkeit im Vergleich zum Stand-der-Technik von 6,13% in Durchschnitt. Der Ansatz passt die meteorologischen Vorhersagen an die lokale Wettersituation entlang der Leitung an. Diese Anpassungen sind aufgrund von Veränderungen der Topographie entlang der Leitungstrasse und Windschatten der umliegenden Bäume notwendig, da durch die meteorologischen Modelle diese nicht beschrieben werden können. Außerdem ist das in dieser Dissertation entwickelte Modell in der Lage die Genauigkeitsabweichungen der Wettervorhersage zwischen Tag und Nacht abzugleichen, was vorteilhaft für die Strombelastbarkeitsprognose ist. Die Zuverlässigkeit und deswegen auch die Effizienz des Stromerzeugungsplans für den nächsten 48 Stunden wurde um 10% gegenüber dem Stand der Technik erhöht. Außerdem wurde in Rahmen dieser Arbeit ein Verfahren für die Positionierung der Wetterstationen entwickelt, um die wichtigsten Stellen entlang der Leitung abzudecken und gleichzeitig die Anzahl der Wetterstationen zu minimieren. Wird ein verteiltes Sensornetzwerk in ganz Deutschland umgesetzt, wird die Einsparung von Redispatchingkosten eine Kapitalrendite von ungefähr drei Jahren bedeuten. Die Durchführung einer transienten Analyse ist im entwickelten System ebenfalls möglich, um Engpassfälle für einige Minuten zu lösen, ohne die maximale Leitertemperatur zu erreichen. Dieses Dokument versucht, die Vorteile der Freileitungsmonitoringssysteme zu verdeutlichen und stellt eine Lösung zur Unterstützung eines flexiblen elektrischen Netzes vor, die für eine erfolgreiche Energiewende erforderlich ist