158 research outputs found
Optimal Operation of Industrial Batch Crystallizers: A Nonlinear Model-based Control Approach
No full textBatch crystallization is extensively employed in the chemical, pharmaceutical, and food industries to separate and purify high value-added chemical substances. Despite their widespread application, optimal operation of batch crystallizers is particularly challenging. The difficulties primarily result from the complexity of process models, uncertainties in crystallization kinetics, sensor limitations for reliable measurement of process variables, and the inherent process uncertainties that may impair the effectiveness of advanced control strategies. In addition, the optimal operation of batch crystallizers is often hampered by lack of process actuators. Nonetheless, advanced control of batch crystallizers offers ample opportunities to effectively respond to the dynamic market demands of crystalline products. This is to realize the stringent product specifications of the consumer-driven market as enhancing the process productivity. In this thesis, a nonlinear model-based control approach is developed to address the inherent challenges of real-time control of existing industrial batch crystallizers. The primary requirement on the control approach is its applicability to a wide range of industrial batch crystallizers. This calls for a generic modeling framework, which allows us to describe the dynamics of diverse crystallization kinetics of any complexity and to incorporate the effect of different actuating mechanisms. The generic framework of the process model necessitates the use of computationally efficient model solution techniques and optimization strategies to ensure the real-time feasibility of the control approach. In addition, model imperfections, along with process disturbances, make the online adaptation of model predictions a prerequisite for successful application of the control approach. In pursuit of the research objective, the contributions of this thesis are structured in three major directions: 1. Population balance modeling: A full population balance modeling framework is developed as the cornerstone of the control approach. The inference of model parameters from experimental data is discussed. In addition, various population balance solution methods are investigated in terms of the performance requirements essential for online control applications. 2. Nonlinear state estimation: The effectiveness of several nonlinear state estimation techniques for output feedback control of industrial batch crystallizers is evaluated. The ability of the state estimators in coping with model imperfections and process uncertainties is examined. 3. Real-time dynamic optimization: The feasibility of real-time dynamic optimization of population balance models is explored using different direct optimization strategies. The research program leads to the design of an output feedback nonlinear model-based control approach. The distinct contribution of this thesis lies in using a full population balance modeling framework, which is essential for the applicability of the control approach to a wide range of industrial batch crystallizers. It is shown that the numerical difficulties of solving the population balance equation can be alleviated by using high order finite volume methods combined with a flux limiting function. These numerical techniques facilitate efficient model solution, which is a prerequisite for real-time control. In addition, it is shown that the multiple shooting strategy is well-suited for online dynamic optimization of population balance models. Successful application of the control approach is demonstrated by several simulation case studies, viz a single-input single-output semi-industrial crystallizer and a multi-input multi-output industrial crystallizer. It is illustrated that model imperfections and process uncertainties are largely detrimental to the performance of the nonlinear model-based controller. The performance inadequacy can be effectively compensated for by using an extended Kalman filter or an unscented Kalman filter with time-varying process noise covariance matrix. The real-time performance of the control approach is demonstrated experimentally by several implementations on a semi-industrial crystallizer. It is shown that lack of actuation may obstruct the optimal operation of industrial crystallizers throughout the entire batch run.Delft Center for Systems and ControlMechanical, Maritime and Materials Engineerin
Monitoring of industrial processes using large scale first principles models
No full textApplied Science
System Identification in Dynamic Networks
No full textSystems in engineering such as power systems, telecommunication systems, and distributed control systems are becoming more complex and interconnected. Many of these systems are part of the foundation of modern society and their seamless operation is paramount. However, the increasing complexity and size of the systems poses real engineering challenges (in maintaining stability of the electrical power grid, increasing data throughput of telecommunication networks, etc.). These systems cannot be operated, designed, and maintained without the help of models. System identification is the art of constructing a model of a system from a given set of measurements. However, the field of system identification is primarily focused on identifying open and closed-loop systems. Recently, there has been a move to considering more complex interconnection structures, enabling the application of system identification tools to a larger variety of systems. In this thesis methods to consistently identify modules embedded in a dynamic network with known interconnection structure (topology) are developed. Thus, for example, using the tools developed in this thesis it is possible to estimate the dynamics of a power station (or an aggregate of generators such as wind, solar, etc.) given measurements taken during regular operation of the power grid. The resulting model enables operators of the grid to make better informed decisions and predictions about the grid. The tools developed in this thesis are quite general can be applied under a wide variety of conditions (such as the presence of process noise and sensor noise for instance). Consequently, it is expected that these methods can even be applied to a large variety of systems outside the engineering domain such as biological and geological systems.Delft Center for Systems and ControlMechanical, Maritime and Materials Engineerin
Unitized curtain wall systems for non-orthogonal façades: Solutions for custom-made unitized curtain wall systems for non-orthogonal façades
No full textSolutions for custom-made unitized curtain wall systems for non-orthogonal facadesFacade masterBuilding TechnologyArchitectur
Batch-to-batch learning for model-based control of process systems with application to cooling crystallization
No full textFrom an engineering perspective, the term process refers to a conversion of raw materials into intermediate or final products using chemical, physical, or biological operations. Industrial processes can be performed either in continuous or in batch mode. There exist for instance continuous and batch units for reaction, distillation, and crystallization. In batch mode, the raw materials are loaded in the unit only at the beginning of the process. Subsequently, the desired transformation takes place inside the unit, and the products are eventually removed altogether after the processing time. In order to obtain the desired production volume, several batches are repeated. In an industrial process, several variables such as temperatures, pressures, and concentrations have to be regulated in order to ensure safety, maintain the product quality, and optimize economic criteria. In principle, model-based control techniques available in the literature could be systematically utilized in order to achieve these goals. However, a limitation to the applicability of model-based techniques for batch process control is that the available models of batch processes often suffer from severe uncertainties. In this thesis, we have investigated the use of measured data in order to improve the performance of model-based control of batch processes. Our approach consists in using the measured data in order to refine from batch to batch the model that is used to design the controller. By doing so, the performance delivered by the model-based controller is expected to improve. We have developed the parametric model update technique Iterative Identification Control (IIC) and non-parametric model update technique Iterative Learning Control (ILC). While in IIC the measured batch data are used to update from batch to batch parameter estimates for the uncertain physical coefficients, in ILC the data are used to compute a non-parametric, additive correction term for a nominal process model. We have tested the ILC and IIC algorithms for the batch cooling crystallization process both in a simulation environment and on a real pilot-scale crystallization setup. We have shown that the two approaches have complementary advantages. On the one hand, the parametric approach allows for a faster learning since it produces a parsimonious representation of the process. On the other hand, the nonparametric approach can cope effectively with the serious issue of structural mismatches owing to the use of a more flexible representation. Furthermore, we have investigated the use of excitation signals to enhance the performance of parametric model update techniques in an iterative identification/controller design scheme similar to IIC. The excitation signals have a dual effect on the overall control performance. On the one hand, the application of an excitation signal superposed to the normal control input leads after identification to an increased model accuracy, and thus a better control performance. On the other hand, the excitation signal also causes a temporary performance degradation, since it acts as a disturbance while it is applied to control system. For linear dynamical systems, we have shown that the problem of designing the excitation signals aiming to maximize the overall control performance can be approximated as a convex optimization problem. The lack of generally applicable and computationally efficient experiment design tools for nonlinear systems is the main bottleneck for the optimal design of the excitation signals in the case of batch processes. In this thesis, we have developed a novel experiment design method applicable to the class of fading memory nonlinear system. Limiting the excitation signals to a finite number of levels, the information matrix can be expressed as a linear function of the frequency of occurrence of each possible pattern having duration equal to the memory of the system. Exploiting the linear relation between the frequencies and the information matrix, several experiment design problems can be formulated as convex optimization problems.DCSCMechanical, Maritime and Materials Engineerin
From data to performance system; identification uncertainty and robust control design
No full textMechanical Maritime and Materials Engineerin
Well testing in the framework of system identification
No full textWell testing has been subject of research for many decades. Well testing is performed to estimate reservoir properties. These estimations are needed to predict the amount of oil that can be produced, and to determine a strategy how to produce this predicted amount. The currently used procedure to estimate the properties seems cumbersome and inefficient and uncertainty regions of the estimated properties are not yet available. In this report the focus is on evaluating the current well test methods from a system identification point of view and on improving the well test analysis. The latest and best performing well test method in literature is evaluated by simulations and the results are compared with results of a new well test analysis method using Prediction Error Identification (PEI). PEI is a black box identification method. Both methods contribute to improvements in the field of well testing. The new method estimates a full expression for the system, consisting of the reservoir and well bore. Direct property estimation, without the interpretation of so called type curves, reliable uncertainty regions and large cost reduction are now within reach.DCSCMechanical, Maritime and Materials Engineerin
Improved Economic Performance of Municipal Solid Waste Combustion Plants by Model Based Combustion Control
No full textThe combustion of municipal solid waste (MSW) is used for its inertisation, reduction of its volume and the conversion of its energy content into heat and/or electricity. Operation and control of modern large scale MSW combustion (MSWC) plants is determined by economic and environmental objectives and constraints that have to be fulfilled in the presence of large unmeasured disturbances resulting from a large variation in waste composition, which make the fulfillment of these objectives and constraints very difficult, and under varying market conditions and, thereby, economic priorities. MSWC plant operators and managers are under an increasing pressure to operate economically more optimally, within the operating envelope determined by the environmental limits, due to the increasing business character of the environment they have to operate in, with market forces and competition increasingly dictating the plant operation. A direction with high potential for obtaining a more optimal economic MSWC plant performance is the application of model based combustion control, which has not been done so far at MSWC plants where PID type of combustion control is the standard. This potential is due to the fact that the combustion control system of an MSWC plant has a major influence on its overall economic performance and that model based control strategies allow for a systematic handling of the main characteristics of the MSWC plant combustion control problem, more specific the presence of constraints and the multivariable, interacting nature of the MSWC plant combustion dynamics. In particular the model based control strategy called Model Predictive Control (MPC) allows for this systematic handling and has potential for improvement. Motivated by the observed need for an improved economic MSWC plant operation and the potential of model based combustion control for achieving this goal, the main research objective addressed in this thesis is to explore the opportunities of this type of control for improving the economic performance of these plants. The main conclusion from this thesis is that the economic performance of an MSWC plant can significantly be improved by means of model based combustion control, in particular by means of MPC based combustion control. More specific, usage of the latter type of combustion control particularly allows for an improved constraint handling of controlled variables not subject to setpoint tracking. This ability can be used to significantly reduce MSWC plant downtime and maintenance costs, e.g. by maintaining furnace temperatures below a certain maximum level to increase the lifetime of furnace components. MPC also allows for a significant reduction in process variability in MSWC plant combustion control applications even when constraints do not come into play, which can be used to decrease operational and maintenance costs and to operate the MSWC plant closer to the economically optimal operating point. Additionally, the ability to include constraints in MPC together with the flexibility in formulating its optimal control problem allows for application of constraint pushing type of MSWC plant combustion control problems, which also allows for operating the MSWC plant closer to the economically optimal operating point.Delft Center for Systems and ControlMechanical, Maritime and Materials Engineerin
Mechatronics and Control Solutions for Increasing the Imaging Speed in Atomic Force Microscopy
No full textThis research is focused on improving the mechatronic design and control strategies of Atomic Force Microscopes (AFM) in order to increase the imaging speed of these instruments, while maintaining the resolution and accuracy of AFM. Different techniques are developed which have led to a faster lateral scanning speed, higher bandwidth control of the tip sample force, and improved accuracy of the topography estimate. An Atomic Force Microscope (AFM) is a mechanical microscope in which the sample is probed by a very sharp tip. AFM allows measuring the sample topography with (sub-) nanometer resolution. As in AFM the sample is probed point by point, AFM imaging is a relatively slow process, which limits the applicability of AFM in fields where high throughput is important. In AFM de sample is scanned in the lateral plane by use of a piezoelectric scanning stage. The lateral scanning speed is limited by the weakly damped resonances of the scanning stage, which may cause strong oscillations when excited. In this research a cost efficient method is developed to dampen the resonances of the scanning stage, resulting in a 30 times faster lateral scanning motion. During scanning the force between the tip and the sample is measured and controlled in a feedback loop to prevent damage of the tip and the sample. Moreover, this feedbackloop provides an estimate of the sample topography via a topography estimator. In this research an approach is presented for integrated design of the feedback controller and topography estimator. This approach explicitly addresses the uncertainty in the dynamical behavior of the instrument. It is shown that due to the uncertainty in the dynamical behavior of the instrument, a design trade-off has to be made between the speed and the accuracy of AFM instruments. In order to further improve the imaging speed of AFM, a dual actuated control approach is investigates to control the force between the tip and the sample via a combination of a long-stroke and a short-stroke actuator. This dual actuated control approach has shown to allow 20 times faster imaging as compared to the optimally controlled single actuated AFM, without compromising the effective positioning range.Delft Center for Systems and ControlMechanical, Maritime and Materials Engineerin
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