Characterisation of Dynamic Process Systems by Use of Recurrence Texture Analysis

This thesis proposes a method to analyse the dynamic behaviour of process systems using sets of textural features extracted from distance matrices obtained from time series data. Algorithms based on the use of grey level co-occurrence matrices, wavelet transforms, local binary patterns, textons, and the pretrained convolutional neural networks (AlexNet and VGG16) were used to extract features. The method was demonstrated to effectively capture the dynamics of mineral process systems and could outperform competing approaches

Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

Experimental study on natural vibration frequency identification of hydraulic concrete structure using concrete piezoceramic smart module

The identification of structural modal parameter is an important link in the dynamics monitoring and diagnosis for the structural health. The passive monitoring mode of piezoceramic is used to solve the natural vibration frequency identification problem of hydraulic concrete structure. Based on self-made concrete piezoelectric smart module (CPSM), a system is developed to obtain the modal parameters of hydraulic concrete structure. The CPSM is regarded as a sensor to monitor passively the structural natural vibration frequency. The method and process are proposed to identify the natural vibration frequency of hydraulic concrete structure. Based on the physical model and numerical simulation model, the rationality and feasibility of the proposed method are verified

Archives of Data Science, Series A. Vol. 1,1: Special Issue: Selected Papers of the 3rd German-Polish Symposium on Data Analysis and Applications

The first volume of Archives of Data Science, Series A is a special issue of a selection of contributions which have been originally presented at the {\em 3rd Bilateral German-Polish Symposium on Data Analysis and Its Applications} (GPSDAA 2013). All selected papers fit into the emerging field of data science consisting of the mathematical sciences (computer science, mathematics, operations research, and statistics) and an application domain (e.g. marketing, biology, economics, engineering)

Modeling, identification and control of a cold flow circulating fluidized bed

Circulating fluidized bed (CFB) is used extensively in petrochemical industries especially for fluid catalytic cracking, coal combustion or gasification and various other chemical processes. In this work, data are used to identify cold flow circulating fluidized bed\u27s (CFCFB) multiple sub models and to combine them into a single nonlinear model such that solids circulation rate can be estimated from the move air flow and riser aeration fed to the device, and the total pressure drop developed across the riser at extremely different experimental conditions.;The present work begins with a complete black box model of a state-space description arising from the system identification and converts it into a model without any fictitious variable such that the interaction among the variables under consideration can be analyzed. Furthermore, this concept separates a state into stochastic and deterministic components which gives the nature of noise acting on the measurement device and rationalizes if there exists a certain relationship between independent and dependent variable. In this thesis, the state is a solids circulation rate. Independent parameters that comprise of aerations flow rates including move air flow, riser aeration and loop seal fluidization air are used to obtain deterministic component of a measured solids circulation rate. On the other hand, easily measurable dependent variables like the pressure drops across various sections of the machine are used to predict its stochastic counterpart.;A real time pressure drop model based on the Recursive Prediction Error Method (RPEM) is built to predict the split of move air flow between the standpipe and L-valve. The split estimate is of paramount importance while simulating the phenomenological model of the standpipe or in other applications, if required. Additional aeration fed across the various sections of standpipe act as the fluidization bias and their routes determination within the component may help to maintain their required level to assist in solids movement during operation while minimizing excessive flows. The path determination is also predicted using RPEM on a discrete time pressure drop model such that the user can operate them at the desired intensity according to their operating requirements.;Generally, a PID controller is not portable , i.e., a controller designed for one plant is usually not applicable to another plant. To resolve this long-standing issue of portable controllable design, the controller scaling method can be used to control similar plants that are different only in gain and frequency scales, thus avoiding tedious control redesign. The adaptive PID control algorithm is then tested on the benchmark NETL CFCFB plant by controlling solids circulation rate according to the reference solids flow rate obtained from the Knowlton\u27s correlation utilizing average voidage in a moving bed condition and the move air flow.;The optimal control of solids circulation rate affecting the heat and mass transfer characteristics which in turn impacts the efficiency of various chemical processes is necessary in CFB units. An example might be the catalytic systems that recirculate catalyst in a reaction/recirculation cycle. In the case of such units in which the addition of catalyst is small and need not be steady, the main solids flow-control problem is to maintain balanced inventories of catalyst in and controlled flow from and to the reactor and regenerator. This flow of solids from an oxidizing atmosphere to a reducing one, or vice versa, usually necessitates stripping gases from the interstices of the solids as well as gases absorbed by the particles. Steam is usually used for this purpose. The point of removal of the solids from the fluidized bed is usually under a lower pressure than the point of feed introduction into the carrier gas. The pressure is higher at the bottom of the solids draw-off pipe due to the relative flow of gas counter to the solids flow. The gas may either be flowing downward more slowly than the solids or upward. The standpipe may be fluidized, or the solids may be in moving packed bed flow with no expansion. Gas is introduced at the bottom (best for group B) or at about 3-m intervals along the standpipe (best for group A). The increasing pressure causes gas inside and between the particles to be compressed. Unless aeration gas is added, the solids could defluidize and become a moving fixed bed with a lower pressure head than that of fluidized solids. Thus, this observation leads to the fact that the gas velocity in the standpipe might be the main parameter to control the solids circulation rate. (Abstract shortened by UMI.)

Modern approaches to control of a multiple hearth furnace in kaolin production

The aim of this thesis is to improve the overall efficiency of the multiple hearth furnace (MHF) in kaolin calcination by developing control strategies which incorporate machine learning based soft sensors to estimate mineralogy related constraints in the control strategy. The objective of the control strategy is to maximize the capacity of the furnace and minimize energy consumption while maintaining the product quality of the calcined kaolin. First, the description of the process of interest is given, highlighting the control strategy currently implemented at the calciner studied in this work. Next, the state of the art on control of calcination furnaces is presented and discussed. Then, the description of the mechanistic model of the MHF, which plays a key role in the testing environment, is provided and an analysis of the MHF dynamic behavior based on the industrial and simulated data is presented. The design of the mineralogy-driven control strategy for the multiple hearth furnace and its implementation in the simulation environment are also outlined. The analysis of the results is then presented. Furthermore, the extensive sampling campaign for testing the soft sensors and the control strategy logic of the industrial MHF is reported, and the results are analyzed and discussed. Finally, an introduction to Model Predictive Control (MPC) is presented, the design of the Linear MPC framework for the MHF in kaolin calcination is described and discussed, and future research is outlined

WEIGH-IN-MOTION DATA-DRIVEN PAVEMENT PERFORMANCE PREDICTION MODELS

The effective functioning of pavements as a critical component of the transportation system necessitates the implementation of ongoing maintenance programs to safeguard this significant and valuable infrastructure and guarantee its optimal performance. The maintenance, rehabilitation, and reconstruction (MRR) program of the pavement structure is dependent on a multidimensional decision-making process, which considers the existing pavement structural condition and the anticipated future performance. Pavement Performance Prediction Models (PPPMs) have become indispensable tools for the efficient implementation of the MRR program and the minimization of associated costs by providing precise predictions of distress and roughness based on inventory and monitoring data concerning the pavement structure\u27s state, traffic load, and climatic conditions. The integration of PPPMs has become a vital component of Pavement Management Systems (PMSs), facilitating the optimization, prioritization, scheduling, and selection of maintenance strategies. Researchers have developed several PPPMs with differing objectives, and each PPPM has demonstrated distinct strengths and weaknesses regarding its applicability, implementation process, and data requirements for development. Traditional statistical models, such as linear regression, are inadequate in handling complex nonlinear relationships between variables and often generate less precise results. Machine Learning (ML)-based models have become increasingly popular due to their ability to manage vast amounts of data and identify meaningful relationships between them to generate informative insights for better predictions. To create ML models for pavement performance prediction, it is necessary to gather a significant amount of historical data on pavement and traffic loading conditions. The Long-Term Pavement Performance Program (LTPP) initiated by the Federal Highway Administration (FHWA) offers a comprehensive repository of data on the environment, traffic, inventory, monitoring, maintenance, and rehabilitation works that can be utilized to develop PPPMs. The LTPP also includes Weigh-In-Motion (WIM) data that provides information on traffic, such as truck traffic, total traffic, directional distribution, and the number of different axle types of vehicles. High-quality traffic loading data can play an essential role in improving the performance of PPPMs, as the Mechanistic-Empirical Pavement Design Guide (MEPDG) considers vehicle types and axle load characteristics to be critical inputs for pavement design. The collection of high-quality traffic loading data has been a challenge in developing Pavement Performance Prediction Models (PPPMs). The Weigh-In-Motion (WIM) system, which comprises WIM scales, has emerged as an innovative solution to address this issue. By leveraging computer vision and machine learning techniques, WIM systems can collect accurate data on vehicle type and axle load characteristics, which are critical factors affecting the performance of flexible pavements. Excessive dynamic loading caused by heavy vehicles can result in the early disintegration of the pavement structure. The Long-Term Pavement Performance Program (LTPP) provides an extensive repository of WIM data that can be utilized to develop accurate PPPMs for predicting pavement future behavior and tolerance. The incorporation of comprehensive WIM data collected from LTPP has the potential to significantly improve the accuracy and effectiveness of PPPMs. To develop artificial neural network (ANN) based pavement performance prediction models (PPPMs) for seven distinct performance indicators, including IRI, longitudinal crack, transverse crack, fatigue crack, potholes, polished aggregate, and patch failure, a total of 300 pavement sections with WIM data were selected from the United States of America. Data collection spanned 20 years, from 2001 to 2020, and included information on pavement age, material properties, climatic properties, structural properties, and traffic-related characteristics. The primary dataset was then divided into two distinct subsets: one which included WIMgenerated traffic data and another which excluded WIM-generated traffic data. Data cleaning and normalization were meticulously performed using the Z-score normalization method. Each subset was further divided into two separate groups: the first containing 15 years of data for model training and the latter containing 5 years of data for testing purposes. Principal Component Analysis (PCA) was then employed to reduce the number of input variables for the model. Based on a cumulative Proportion of Variation (PoV) of 96%, 12 input variables were selected. Subsequently, a single hidden layer ANN model with 12 neurons was generated for each performance indicator. The study\u27s results indicate that incorporating Weigh-In-Motion (WIM)-generated traffic loading data can significantly enhance the accuracy and efficacy of pavement performance prediction models (PPPMs). This improvement further supports the suitability of optimized pavement maintenance scheduling with minimal costs, while also ensuring timely repairs to promote acceptable serviceability and structural stability of the pavement. The contributions of this research are twofold: first, it provides an enhanced understanding of the positive impacts that high-quality traffic loading data has on pavement conditions; and second, it explores potential applications of WIM data within the Pavement Management System (PMS)

Systems and control : 21th Benelux meeting, 2002, March 19-21, Veldhoven, The Netherlands

Book of abstract

Corrosion and Degradation of Materials

Studies on the corrosion and degradation of materials play a decisive role in the novel design and development of corrosion-resistant materials, the selection of materials used in harsh environments in designed lifespans, the invention of corrosion control methods and procedures (e.g., coatings, inhibitors), and the safety assessment and prediction of materials (i.e., modelling). These studies cover a wide range of research fields, including the calculation of thermodynamics, the characterization of microstructures, the investigation of mechanical and corrosion properties, the creation of corrosion coatings or inhibitors, and the establishment of corrosion modelling. This Special Issue is devoted to these types of studies, which facilitate the understanding of the corrosion fundamentals of materials in service, the development of corrosion coatings or methods, improving their durability, and eventually decreasing corrosion loss

Machine vision in measurement and control of mineral concentration process

This thesis considers machine vision in the context of the mining, mineral and metal industry (MMMI). Even though MMMI might be seen as a rather conservative industry branch, in many cases it is not. One motivation for constant research and development is the large amount of ore processed on a yearly basis, which means that even a slight improvement in performance can lead to substantial economical benefits. Another point, related more closely to the thesis, is that the development in camera and information technology has enabled the integration of machine vision based applications into many different industry branches, MMMI being one of them. Machine vision and its utilization in measurement and control of a modern flotation plant is studied in detail. The research was started in the late 90's with the development of an image analysis platform for flotation froths, which was later extended to cover multiple flotation cells. The resulting image analysis based variables were studied and new results regarding their usefulness both in single and multi-camera settings were obtained. The most important variables are shown to the plant operators and used in closed loop control. Furthermore, an image history database and a tool for its utilization were created, as well as a new type of froth level measurement technique introduced. The research done with the image analysis of flotation froths provided strong evidence of the importance of the froth colour as an indicator of grade. This motivated further studies carried out with a spectrophotometer, which is a more accurate instrument for colour measurements. As a result, a new type of on-line measurement technique was created to be used as a supplement to existing X-Ray fluorescence (XRF) analyzers to reduce their typical sampling interval of 10-20 minutes to a virtually continuous measurement. Another field of research presented is the particle size distribution analysis of crushed ore from a moving conveyor belt in a contact-free manner, for which two new measurement techniques are presented. This information, when measured already in the mine, can be used in the flotation plant to gain better grinding results, and geologists can use it in mine planning