A simplified model based supercritical power plant controller

We present a simplified state-space model of a once-through supercritical boiler turbine power plant. This phenomenological model has been developed from a greatly simplified application of the first principles of physical laws. When we fit our model to a far more complex and physically accurate simulation model commissioned by EPRI for operator training, we find that the input-output responses are surprisingly close. Encouraged by this initial success, we describe some initial steps toward a design method for supercritical boiler control suggested by the geometric structure arising from the simplified model. Preliminary simulation results suggest that this approach may offer a closed loop response considerably improved relative to that achieved by the linear controllers presently in place in typical industrial settings

Integration of CasADi and JModelica.org

This paper presents the integration of two open source softwares: CasADi, which is a framework for efficient evaluation of expressions and their derivatives, and the Modelica-based platform JModelica.org. The integration of the tools is based on an XML format for exchange of DAE models. The JModelica.org platform supports export of model in this XML format, whereas CasADi supports import of models expressed in this format. Furthermore, we have carried out comparisons with ACADO, which is a multiple shooting package for solving optimal control problems. CasADi, in turn, has been interfaced with ACADO Toolkit, enabling users to define optimal control problems using Modelica and Optimica specifications, and use solve using direct multiple shooting. In addition, a collocation algorithm targeted at solving large- scale DAE constrained dynamic optimization problems has been implemented. This implementation explores CasADi’s Python and IPOPT interfaces, which offers a convenient, yet highly efficient environment for development of optimization algorithms. The algorithms are evaluated using industrially relevant benchmark problems

Preference driven multi-objective optimization design procedure for industrial controller tuning

Multi-objective optimization design procedures have shown to be a valuable tool for con- trol engineers. These procedures could be used by designers when (1) it is difficult to find a reasonable trade-off for a controller tuning fulfilling several requirements; and (2) if it is worthwhile to analyze design objectives exchange among design alternatives. Despite the usefulness of such methods for describing trade-offs among design alterna- tives (tuning proposals) with the so called Pareto front, for some control problems finding a pertinent set of solutions could be a challenge. That is, some control problems are com- plex in the sense of finding the required trade-off among design objectives. In order to improve the performance of MOOD procedures for such situations, preference handling mechanisms could be used to improve pertinency of solutions in the approximated Pareto front. In this paper an overall MOOD procedure focusing in controller tuning applications using designer s preferences is proposed. In order to validate such procedure, a bench- mark control problem is used and reformulated into a multi-objective problem statement, where different preference handling mechanisms in the optimization process are evalu- ated and compared. The obtained results validate the overall proposal as a potential tool for industrial controller tuning.This work was partially supported by projects TIN2011-28082, ENE2011-25900 from the Spanish Ministry of Economy and Competitiveness. First author gratefully acknowledges the partial support provided by the postdoctoral fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil (CNPq) for the development of this work.Reynoso Meza, G.; Sanchís Saez, J.; Blasco Ferragud, FX.; Martínez Iranzo, MA. (2016). Preference driven multi-objective optimization design procedure for industrial controller tuning. Information Sciences. 339:108-131. doi:10.1016/j.ins.2015.12.002S10813133

Data-driven model predictive control using random forests for building energy optimization and climate control

Model Predictive Control (MPC) is a model-based technique widely and successfully used over the past years to improve control systems performance. A key factor prohibiting the widespread adoption of MPC for complex systems such as buildings is related to the difficulties (cost, time and effort) associated with the identification of a predictive model of a building. To overcome this problem, we introduce a novel idea for predictive control based on historical building data leveraging machine learning algorithms like regression trees and random forests. We call this approach Data-driven model Predictive Control (DPC), and we apply it to three different case studies to demonstrate its performance, scalability, and robustness. In the first case study we consider a benchmark MPC controller using a bilinear building model, then we apply DPC to a data-set simulated from such bilinear model and derive a controller based only on the data. Our results demonstrate that DPC can provide comparable performance with respect to MPC applied to a perfectly known mathematical model. In the second case study, we apply DPC to a 6 story 22 zone building model in EnergyPlus, for which model-based control is not economical and practical due to extreme complexity, and address a Demand Response problem. Our results demonstrate scalability and efficiency of DPC showing that DPC provides the desired power curtailment with an average error of 3%. In the third case study, we implement and test DPC on real data from an off-grid house located in L’Aquila, Italy. We compare the total amount of energy saved with respect to the classical bang-bang controller, showing that we can perform an energy saving up to 49.2%. Our results demonstrate the robustness of our method to uncertainties both in real data acquisition and weather forecast

Multivariable control of a steam boiler

This thesis is devoted to apply a Multi-Input Multi-Output (MIMO) controller to a specific Steam Boiler Plant. The considered plant is based on the descriptions obtained from the input/output data of a referenced steam boiler in the Abbot combined cycle plant in Champaign, Illinois. The objective is to take all the useful input/output data from the steam boiler according to its performance and capability in different operation points in order to model the most accurate plant for control. The conceived case of study is based in a modification of a model proposed by Pellegrinetti and Bentsman in 1996, considering to be tested under a benchmark proposed by the Control Spanish Association (CEA). Initially, taking into account only the input and output data of the system, black box modeling techniques were used to obtain different models of the plant. The first approach was to obtain a transfer function model to apply a Internal Model Controller (IMC). However the result was not as expected because the controller becomes considerable difficult to tune given the big quantity of poles and zeros of the resulting IMC controller. Hence this technique was dismissed. On a second stage, it was obtained a model of the plant in state space representation to apply a Linear-Quadratic Regulator (LQR) technique to understand how the system behaves with this state space model design. Given that the description of the system in this form was more accurate the obtained results were better for this type of controller making it better suited to fulfill the needs of the plant. This work covers all the steps followed to use the Internal Model Controller (IMC) and the Linear-Quadratic Regulator (LQR) techniques to study the behavior of a steam boiler system in an industrial environment. The obtained results are exposed and explained with the aim of describing which one of the two used methods is better suited for the control of the plant. Finally a budget and impact studies are presented to explain which could be the resources needed in order to apply this type of controllers effectively in a steam boiler plant, being able to extrapolate the obtained results to be applied to other type of processes in the same sector (heat exchangers, distillation columns, etc.)

Multivariable control of a steam boiler

This thesis is devoted to apply a Multi-Input Multi-Output (MIMO) controller to a specific Steam Boiler Plant. The considered plant is based on the descriptions obtained from the input/output data of a referenced steam boiler in the Abbot combined cycle plant in Champaign, Illinois. The objective is to take all the useful input/output data from the steam boiler according to its performance and capability in different operation points in order to model the most accurate plant for control. The conceived case of study is based in a modification of a model proposed by Pellegrinetti and Bentsman in 1996, considering to be tested under a benchmark proposed by the Control Spanish Association (CEA). Initially, taking into account only the input and output data of the system, black box modeling techniques were used to obtain different models of the plant. The first approach was to obtain a transfer function model to apply a Internal Model Controller (IMC). However the result was not as expected because the controller becomes considerable difficult to tune given the big quantity of poles and zeros of the resulting IMC controller. Hence this technique was dismissed. On a second stage, it was obtained a model of the plant in state space representation to apply a Linear-Quadratic Regulator (LQR) technique to understand how the system behaves with this state space model design. Given that the description of the system in this form was more accurate the obtained results were better for this type of controller making it better suited to fulfill the needs of the plant. This work covers all the steps followed to use the Internal Model Controller (IMC) and the Linear-Quadratic Regulator (LQR) techniques to study the behavior of a steam boiler system in an industrial environment. The obtained results are exposed and explained with the aim of describing which one of the two used methods is better suited for the control of the plant. Finally a budget and impact studies are presented to explain which could be the resources needed in order to apply this type of controllers effectively in a steam boiler plant, being able to extrapolate the obtained results to be applied to other type of processes in the same sector (heat exchangers, distillation columns, etc.)

Model predictive fuzzy control of a steam boiler

This thesis is devoted to apply a Model Predictive Fuzzy Controller (MPC and Takagi-Sugeno) to a specific Steam Boiler Plant. This is a very common problem in control. The considered plant is based on the descriptions obtained from the data of a referenced boiler in the combined cycle plant as Abbot in Champaign, Illinois. The idea is to take all the useful data from the boiler according to its performance and capability in different operation points in order to model the most accurate plant for control. The considered case study is based in a modification of a model proposed by Pellegrinetti and Bentsman in 1996, considering to be tested under the demands of the Control Engineering Association (CEA). The system is Multi-Input and Multi-Output (MIMO), where each controlled output has a specific weight in order to measure the performance. The objective is to minimize cost index but also make it operative and robust for a wide range of variables, discovering the limits of the plant and its behaviour. The model is supposed to manage real data and was constructed under real physical descriptions. However, this model is not a white box, so the analysis and development of the model to be used with the MPC strategy have to be identified to continue with the evaluation of the controlled plant. There are some physical variables that have to be taken into account (Drum Pressure, Excess of Oxygen, Water Level, Water Flow, Fuel Flow, Air Flow and Steam Demand) to know if these variables and other parameters are evolving in the correct way and satisfy the logic of the mass and energy balances in the system. After measuring and analysing the data, the model is validated testing it for different values of steam demands. The controller is tuned for every one of the considered demands. Once tuned, the controller computes the manipulated variables receiving information from the controlled ones, including their references. Finally, the resulting controller is a combination of a set of local controllers using the Takagi-Sugeno approach using the steam demand setpoint as scheduling variable. To apply this approach, a set of local models approximating the non-linear boiler behaviour around a set of steam demand set-points are obtained and then their a fused using the Takagi-Sugeno approach to approximate any unknown steam demand located in the valid range of values

Model compendium, data, and optimization benchmarks for sector-coupled energy systems

Decarbonization and defossilization of energy supply as well as increasing decentralization of energy gen- eration necessitate the development of efficient strategies for design and operation of sector-coupled energy systems. Today, design and operation of process and energy systems rely on powerful numeri- cal methods, in particular, optimization methods. The development of such methods benefits from re- producible benchmarks including transparent model equations and complete input data sets. However, to the authors’ best knowledge and with respect to design and optimal control of sector-coupled en- ergy systems, there is a lack of available benchmarks. Hence, this article provides a model compendium, exemplary realistic data sets, as well as two case studies (i.e., optimization benchmarks) for an in- dustrial/research campus in an open-source description. The compendium includes stationary, quasi- stationary, and dynamic models for typical components as well as linearization schemes relevant for optimization of design, operation, and control of sector-coupled energy systems

Deep Learning-Based, Passive Fault Tolerant Control Facilitated by a Taxonomy of Cyber-Attack Effects

In the interest of improving the resilience of cyber-physical control systems to better operate in the presence of various cyber-attacks and/or faults, this dissertation presents a novel controller design based on deep-learning networks. This research lays out a controller design that does not rely on fault or cyber-attack detection. Being passive, the controller’s routine operating process is to take in data from the various components of the physical system, holistically assess the state of the physical system using deep-learning networks and decide the subsequent round of commands from the controller. This use of deep-learning methods in passive fault tolerant control (FTC) is unique in the research literature. The proposed controller is applied to both linear and nonlinear systems. Additionally, the application and testing are accomplished with both actuators and sensors being affected by attacks and /or faults