Improving paper machine clothing supplier's industrial internet offering with artificial intelligence

Overall amount of data has grown exponentially during the last few years. The increase in the availability of data has driven companies and countries towards digitalization with growing pace. Therefore, the industrial internet applications have become more successful than ever. These applications provide companies more tools to utilize data-driven decisions. In paper industry, paper machine original equipment manufacturers have started to utilize the industrial internet capabilities with increasing pace. The increasing competition has led to the fact that today, utilization of the possibilities offered by industrial internet is part of target organization’s (Valmet) main strategies. Thus, the paper machine clothing (PMC) unit of Valmet has commissioned this thesis work. The goal of this research was to improve Valmet’s PMC unit’s industrial internet offering. Improvement actions taken were to enhance the existing offering through customer feedback and to provide additional value with artificial intelligence. The approach towards the subject was to find out the existing theory behind the operational context of the fabrics, discover possible developmental actions through prototyping and by creating value-adding AI models to support the offering. During this research process it came evidently clear that the initial industrial internet applications would have good applicability in pilot customer’s daily routines. Though good developmental points were discovered from the prototyping phase, the functionality issues of the initial industrial internet applications during the timeframe of this thesis limited the quality of the feedback. More thorough study for customer feedback should be conducted after the applications have been in daily use for solid amount of time. This research provided two value-adding models for industrial internet applications. The idea for the models sprung from the hopes of the target company. Initially, fabric delivery cycles have been defined more or less by hand. Thus, the Monte Carlo simulation to optimize delivery cycles and to manage risk governing possible shortages was illustrated as the first model. The second model aimed to enhance the first model by conducting estimations of remaining fabric lifetime from customer’s mill’s process data. Neural network was chosen as the machine learning method for this model. Both models were tested with actual process data and the results of the case study were polarized. The simulation model provided valid results and first indications showed that it would bring true added value to the target organization. However, the results of the second model indicated that with available data valid results were not acquired. The results of this study indicate that the artificial intelligence models can be utilized to fabrics industrial internet but more emphasis should be pointed on the comparison of different machine learning methods and to enhance the quality and quantity of the available data

Probabilistic and artificial intelligence modelling of drought and agricultural crop yield in Pakistan

Pakistan is a drought-prone, agricultural nation with hydro-meteorological imbalances that increase the scarcity of water resources, thus, constraining water availability and leading major risks to the agricultural productivity sector and food security. Rainfall and drought are imperative matters of consideration, both for hydrological and agricultural applications. The aim of this doctoral thesis is to advance new knowledge in designing hybridized probabilistic and artificial intelligence forecasts models for rainfall, drought and crop yield within the agricultural hubs in Pakistan. The choice of these study regions is a strategic decision, to focus on precision agriculture given the importance of rainfall and drought events on agricultural crops in socioeconomic activities of Pakistan. The outcomes of this PhD contribute to efficient modelling of seasonal rainfall, drought and crop yield to assist farmers and other stakeholders to promote more strategic decisions for better management of climate risk for agriculturalreliant nations

Fault Diagnosis Via Univariate Frequency Analysis Monitoring: A Novel Technique Applied to a Simulated Integrated Drive Generator

The purpose of this research was to develop a fault detection and diagnostic method that would be able to detect and isolate seeded faults in data that was generated from a simulated integrated drive generator. The approach to the solution for this problem is summarized below. A novel approach for the detection and diagnoses of an anomaly due the occurrence of a fault within a system has been developed. This innovative technique uses specific characteristics of the frequency spectrum of a univariate signal to monitor system health for abnormal behavior due to previously characterized component failure. A fault detection and diagnostic scheme was developed that used dual heteroassociative kernel regression models. The first of these empirical models estimates selected features from the analytical redundant spectrum characteristic profile of the exciter current using power demand, a stressor, placed on the system as input query. The predicted spectrum features were compared to the actual characteristic features, which resulted in the generation of a residual signal. This signal was then analyzed in order to determine if they were the result of normal system disturbances or a predefined fault. If a fault was detected, the residual signal was passed to the second model, which isolated, and given enough information, identified the specific component of components causing the anomaly. Two case studies are presented to illustrate the capability to detect, isolate, and identify a system anomaly. As demonstrated, the monitoring of the frequency spectrum of a single variable can provide adequate indication of equipment health. With the availability of the appropriate data, as in the first case, it is possible for the development of three-layer detection and diagnostic systems that provides fault detection, isolation, and identification. A three-layer detection and diagnostic system is essential in the development of more advance health monitoring and prognostic systems. Despite some shortcomings in the simulated data made available for this work, this method is believed to be applicable to data that more realistically captures real-world relationships, including sensor noise and faults that grow with time

What is the willingness to pay for green electricity in Norway? A perspective on Guarantees of Origin.

Norway is one of the only countries in the world producing its electricity from almost only renewable resources. The Renewable Energy Directive 2001/77/EC (2001) introduced a system of Guarantees of Origin (GOs) as an incentive system for power producers and a tracking system of the renewable electricity consumption in Europe. It is mandatory to purchase GOs to be able to claim any renewable electricity consumption. A low demand for GOs results in only 18 percent (2019) of the renewable electricity being purchased in Norway and the remainings being exported in Europe making Norway the largest exporter of GOs. Consequently, the electricity consumed by Norwegians is not as renewable as believed. Thus, in this thesis we tried to figure out if the low demand has roots in low knowledge about GOs or not. Furtheremore, we aimed to estimate their maximum willingness to pay and factors affecting it. In order to achieve these aims, we used contingent valuation method survey. The survey introduces GOs through a scenario and simplified example, followed by a payment card as the elicitation method. The data is further analyzed by Logistic Regression, Ordinal Logistic Regression, and Interval Regression to gain more in-depth insight about factors affecting willingness to buy (WTB) and willingness to pay (WTP). The results show that most respondents are neither aware of Norway’s green electricity production nor GOs. Nevertheless, after being informed about GOs, most of the respondents without prior knowledge were willing to buy these with an average WTP of 5 to 9 percent of their electricity bill. The most critical factors affecting respondents’ WTB are gender, age, heating source, social media behavior, beliefs and behaviors towards the environment, car type, and prior knowledge about GOs. The models regarding WTP indicate that the most vital factors are education, heating source, employment status, beliefs and behavior toward the environment, social media behavior, and satisfaction with the electricity provider

The Performance of German Firms in the Business-Related Service Sectors: A Dynamic Analysis

We analyze the performance of firms in the German business-related services sector. A quarterly business survey provides the panel data base of our study. Firm performance is measured by the survey respondents’ ordinal indication of their changes in total sales. We use a first-order Markov chain and a multinomial logit specification to model the transition probabilities. Three variants of the model are estimated: a linear index model with and without unobserved firm heterogeneity and a semiparametric model. Main results are that firm size has a positive effect on firm performance, that young firms outperform older competitors, that a bank-relationship with a single creditor has a stabilizing effect and that the degree of diversification has a negative impact on firm performance. The legal status appears to have no significant effect.Service sector, business survey, firm performance, Markov chain

The performance of German firms in the business-related service sectors : a dynamic analysis

We analyze the performance of firms in the German business-related services sector. A quarterly business survey provides the panel data base of our study. Firm performance is measured by the survey respondents? ordinal indication of their changes in total sales. We use a firstorder Markov chain and a multinomial logit specification to model the transition probabilitites. Three variants of the model are estimated: a linear index model with and without unobserved firm heterogeneity and a semiparametric model. Main results are that firm size has a positive effect on firm performance, that young firms outperform older competitors, that a bank-relationship with a single creditor has a stabilizing effect and that the degree of diversification has a negative impact on firm performance. The legal status appears to have no significant effect. --Markov chain,service sector,business survey,firm performance,multinomial logit model,generalized additive model

Dissertation Mentor Communication Style and Behavior as Predictors of Student Stress and Satisfaction

Many graduate students (60%) do not complete their program of study. It is important for universities to find ways to increase student completion rate. The general problem is that online U.S. universities are faced with a high rate of PhD student drop out resulting in an increased number of students not being able to complete their doctoral studies. The purpose of this multiple linear regression study was to identify predictor variables of dissertation student stress and overall dissertation satisfaction. Deci and Ryan\u27s self determination theory and Lazarus\u27 theory of cognitive appraisal were used to guide this research to identify how student perception of mentor communication styles can be used to predict how students appraise stress and overall satisfaction with dissertation. A convenience sample of 178 dissertation students identified through several online dissertation student support and student-led Facebook groups completed the online survey. According to study results, student perception of questioning and preciseness as mentor communication styles predicted significantly lower scores of student appraisal of stress experienced in dissertation. However, student perception of verbal aggressiveness as a mentor communication style predicted significantly higher scores of student stress. Mentor behaviors of academic assistance, mentoring abilities, and personal connection predicted significantly higher levels of overall student dissertation satisfaction. Positive social change initiatives formed by faculty and staff can be made to educate dissertation chairpersons about the communication style and behaviors that are the most effective in mentoring dissertation students

Power System Stability Analysis using Neural Network

This work focuses on the design of modern power system controllers for automatic voltage regulators (AVR) and the applications of machine learning (ML) algorithms to correctly classify the stability of the IEEE 14 bus system. The LQG controller performs the best time domain characteristics compared to PID and LQG, while the sensor and amplifier gain is changed in a dynamic passion. After that, the IEEE 14 bus system is modeled, and contingency scenarios are simulated in the System Modelica Dymola environment. Application of the Monte Carlo principle with modified Poissons probability distribution principle is reviewed from the literature that reduces the total contingency from 1000k to 20k. The damping ratio of the contingency is then extracted, pre-processed, and fed to ML algorithms, such as logistic regression, support vector machine, decision trees, random forests, Naive Bayes, and k-nearest neighbor. A neural network (NN) of one, two, three, five, seven, and ten hidden layers with 25%, 50%, 75%, and 100% data size is considered to observe and compare the prediction time, accuracy, precision, and recall value. At lower data size, 25%, in the neural network with two-hidden layers and a single hidden layer, the accuracy becomes 95.70% and 97.38%, respectively. Increasing the hidden layer of NN beyond a second does not increase the overall score and takes a much longer prediction time; thus could be discarded for similar analysis. Moreover, when five, seven, and ten hidden layers are used, the F1 score reduces. However, in practical scenarios, where the data set contains more features and a variety of classes, higher data size is required for NN for proper training. This research will provide more insight into the damping ratio-based system stability prediction with traditional ML algorithms and neural networks.Comment: Masters Thesis Dissertatio

Evaluation and Analysis of the Photovoltaic Potential for Odisha

Solar energy is a potential resource for the various renewable energy options which is clean, inexhaustible and eco-friendly. The development of usage and installation of PV system needs a relevant solar policy making plan through proper assessment of solar PV Energy potential. The study uses the estimate of the PV potential of an area under consideration using the PVGIS online software. The study divides the total geographic area of into a grid of „mxn‟. The PVGIS evaluated the value of incident solar radiation and generated PV energy at central coordinate of each grid. The evaluation of energy potential for four cases (based on mounting and tracking) uses two critical parameters: annual incident Global radiation and annual PV Energy production. A methodology is presented to plot the rasterized maps of the solar energy potential. The study further discusses a case study of Odisha to show the usefulness of the proposed methodology to develop a district wise strategy for promoting the installation of grid-connected PV system. The decision to install a PV plant depends on three major factors: the climatic and environment conditions of the location, the viability of commercial operations, and the government policies. Considering uncertain nature of geographical parameters development of a reliable model to predict the energy output of a plant-to-be installed becomes essential. The study proposes models that consider only two meteorological variables collected from 1195 locations of Odisha: total annual incident global radiation on the PV module and annual average air temperature. The thesis focuses on simplification at every stage of the development while validating the preciseness of the model. A case study of NIT Rourkela is considered to apply a various methods for the evaluation of PV potential. Again the current solar policy framework of India is reviewed along with the challenges the nation has to face for achieving the targets