Study on Control of CSTR Using Intelligent Strategies

A non linearity, interaction and complexity in modeling lead to a great difficulties in CSTR control system, this paper interested in CSTR control using PID, fuzzy logic and intelligent control strategies. Water flow rate and ethyl acetate were selected as manipulated variables while sodium acetate and reactor temperature as controlled variables. Firstly the system identification was conducted and the results show that the multi input multi output system can be represented by the following matrix The compare among the strategies show preference of fuzzy control

Development of a new comprehensive predictive modeling and control framework for multiple-input, multiple-output processes

The increase in the competitiveness of chemical process industries has necessitated the need for lowered energy and raw material consumption and improved quality control with tighter limits. This stronger control of the process conditions has generated interest in the use of advanced process control, and Model Predictive Control (MPC) is one such approach. The idea behind MPC is the use of a model to predict future behavior and use that knowledge to manipulate the input variables so that a cost function is minimized, resulting in optimal control. The predictive model is the core of the MPC method, and the success of a particular strategy hence depends on the accuracy of the model. The task of model building is also very challenging and time-consuming, so an ideal modeling approach would be one that does not require too much data but maintains its accuracy. The semi-empirical modeling approach has the strength that by using an intelligent model form, it has minimal data requirements. The use of semi-empirical models was first demonstrated by Rollins et al., and they coined the term SET (semi-empirical approach) for their method. The accuracy of SET over conventional empirical models was one of the biggest advantages of that approach. The ease of model identification, robustness in parameters, and a novel algorithm were some of the other strengths. Thus SET showed great potential for use in a multivariate situation, and the results of this work have shown that SET has been able to handle this challenge successfully. This extension led to the creation of a comprehensive modeling framework, which is far superior to the current modeling approach using the semi-empirical models. The various components of this approach are: use of statistical design of experiments, model identification, a novel model structure, and finally an algorithm that seeks to maximize accuracy

Innovative solar energy technologies and control algorithms for enhancing demand-side management in buildings

The present thesis investigates innovative energy technologies and control algorithms for enhancing demand-side management in buildings. The work focuses on an innovative low-temperature solar thermal system for supplying space heating demand of buildings. This technology is used as a case study to explore possible solutions to fulfil the mismatch between energy production and its exploitation in building. This shortcoming represents the primary issue of renewable energy sources. Technologies enhancing the energy storage capacity and active demand-side management or demand-response strategies must be implemented in buildings. For these purposes, it is possible to employ hardware or software solutions. The hardware solutions for thermal demand response of buildings are those technologies that allow the energy loads to be permanently shifted or mitigated. The software solutions for demand response are those that integrate an intelligent supervisory layer in the building automation (or management) systems. The present thesis approaches the problem from both the hardware technologies side and the software solutions side. This approach enables the mutual relationships and interactions between the strategies to be appropriately measured. The thesis can be roughly divided in two parts. The first part of the thesis focuses on an innovative solar thermal system exploiting a novel heat transfer fluid and storage media based on micro-encapsulated Phase Change Material slurry. This material leads the system to enhance latent heat exchange processes and increasing the overall performance. The features of Phase Change Material slurry are investigated experimentally and theoretically. A full-scale prototype of this innovative solar system enhancing latent heat exchange is conceived, designed and realised. An experimental campaign on the prototype is used to calibrate and validate a numerical model of the solar thermal system. This model is developed in this thesis to define the thermo-energetic behaviour of the technology. It consists of two mathematical sub-models able to describe the power/energy balances of the flat-plate solar thermal collector and the thermal energy storage unit respectively. In closed-loop configuration, all the Key Performance Indicators used to assess the reliability of the model indicate an excellent comparison between the system monitored outputs and simulation results. Simulation are performed both varying parametrically the boundary condition and investigating the long-term system performance in different climatic locations. Compared to a traditional water-based system used as a reference baseline, the simulation results show that the innovative system could improve the production of useful heat up to 7 % throughout the year and 19 % during the heating season. Once the hardware technology has been defined, the implementation of an innovative control method is necessary to enhance the operational efficiency of the system. This is the primary focus of the second part of the thesis. A specific solution is considered particularly promising for this purpose: the adoption of Model Predictive Control (MPC) formulations for improving the system thermal and energy management. Firstly, this thesis provides a robust and complete framework of the steps required to define an MPC problem for building processes regulation correctly. This goal is reached employing an extended review of the scientific literature and practical application concerning MPC application for building management. Secondly, an MPC algorithm is formulated to regulate the full-scale solar thermal prototype. A testbed virtual environment is developed to perform closed-loop simulations. The existing rule-based control logic is employed as the reference baseline. Compared to the baseline, the MPC algorithm produces energy savings up to 19.2 % with lower unmet energy demand

Fault detection and root cause diagnosis using dynamic Bayesian network

This thesis presents two real time process fault detection and diagnosis (FDD) techniques incorporating process data and prior knowledge. Unlike supervised monitoring techniques, both these methods can perform without having any prior information of a fault. In the first part of this research, a hybrid methodology is developed combining principal component analysis (PCA), Bayesian network (BN) and multiple uncertain (likelihood) evidence to improve the diagnostic capacity of PCA and existing PCA-BN schemes with hard evidence based updating. A dynamic BN (DBN) based FDD methodology is proposed in the later part of this work which provides detection and accurate diagnosis by a single tool. Furthermore, fault propagation pathway is analyzed using the predictive feature of a BN and cause-effect relationships among the process variables. Proposed frameworks are successfully validated by applying to several process models

Early warning generation for process with unknown disturbance

Process safety has paramount importance in a chemical process. A well designed control system is the first layer in a process system. The warning system works as the upper protection layer above the control system. It alerts the operators when the control system fails to prevent an undesired situation. A typical warning system issues warnings when a monitored variable exceeds the threshold. Often these do not allow operators sufficient lead-time to take corrective actions. With the motivation of improving the operator’s working environment by providing lead-time, the current research develops a predictive warning scheme using a moving horizon technique. The main hypothesis proposed in this thesis is given the current state of process system, the future states of the system can be predicted using a suitable model of process system. If an external input disturbs the system state, the controller will try to bring the system within the desired control/safety limits of the system. A warning is issued if it is determined that the control system will not be able to keep the system withing the safety limits. Based on the hypothesis, warning systems were developed for both linear and nonlinear systems. For linear systems, using the gain of the models, a linear constrained optimization problem was formulated. Linear programming (LP) was used to determine if the system will remain within the safety limits or not. In case the LP determines that there is no feasible solution within the constrained limits, warnings are issued. The predictive warning scheme was also extended for nonlinear systems. A non-linear receding horizon predictor was used to predict the future states of the nonlinear system. However, for nonlinear system formulation leads to nonlinear constrained optimization problem, where the constraints are the safety limits. Controller’s ability to keep the predicted states inside the safety limit was checked using a feasibility test algorithm. The algorithm uses a constraint separation method with weighting functions to determine the existence of a feasible solution. The algorithm calculates the global minimum of the objective function. If the global minimum of the objective function is positive, it signifies no feasible solution within the input and output constraints of the system and a warning is issued. Prediction of the effect of the disturbances requires the knowledge of the disturbances. In process industries, disturbances are often unmeasured. This thesis also investigates the estimation of unknown disturbances. An iterative Expectation Minimization (EM) algorithm was proposed for the estimation of the unknown states and disturbances of nonlinear systems. Efficacy of the proposed methods was shown through a number of case studies. The warning system for the linear system was simulated on a virtual plant of a continuous stirred tank heater (CSTH). The nonlinear warning system was implemented on a continuous stirred tank reactor (CSTR). Both case studies showed that, the proposed method was capable of providing a warning earlier than the traditional methods that issues warning based on the measured signals

Soft sensor development and process control of anaerobic digestion

This thesis focuses on soft sensor development based on fuzzy logic used for real time online monitoring of anaerobic digestion to improve methane output and for robust fermentation. Important process parameter indicators such as pH, biogas production, daily difference in pH and daily difference in biogas production were used to infer alkalinity, a reliable indicator of process stability. Additionally, a fuzzy logic and a rule-based controller were developed and tested with single stage anaerobic digesters operating with cow slurry and cellulose. Alkalinity predictions from the fuzzy logic algorithm were used by both controllers to regulate the organic loading rate that aimed to optimise the biogas process. The predictive performance of a software sensor determining alkalinity that was designed using fuzzy logic and subtractive clustering and was validated against multiple linear regression models that were developed (Partner N° 2, Rothamsted Research 2010) for the same purpose. More accurate alkalinity predictions were achieved by utilizing a fuzzy software sensor designed with less amount of data compared to a multiple linear regression model whose design was based on a larger database. Those models were utilised to control the organic loading rate of a twostage, semi-continuously fed stirred reactor system. Three 5l reactors without support media and three 5l reactors with different support media (burst cell reticulated polyurethane foam coarse, burst cell reticulated polyurethane foam medium and sponge) were operated with cow slurry for a period of seven weeks and twenty weeks respectively. Reactors with support media were proven to be more stable than the reactors without support media but did not exhibit higher gas productivity. Biomass support media were found to influence digester recovery positively by reducing the recovery period. Optimum process parameter ranges were identified for reactors with and without support media. Increased biogas production was found to occur when the loading rates were 3-3.5g VS/l/d and 4-5g VS/l/d respectively. Optimum pH ranges were identified between 7.1-7.3 and 6.9-7.2 for reactors with and without support media respectively, whereas all reactors became unstable at ph<6.9. Alkalinity levels for system stability appeared to be above 3500 mg/l of HCO3 - for reactors without media and 3480 mg/l of HCO3 - for reactors with support media. Biogas production was maximized when alkalinity was 3 between 3500-4500 mg/l of HCO3 - for reactors without support media and 3480- 4300 mg/l of HCO3 - for reactors with support media. Two fuzzy logic models predicting alkalinity based on the operation of the three 5l reactors with support media were developed (FIS I, FIS II). The FIS II design was based on a larger database than FIS I. FIS II performance when applied to the reactor where sponge was used as the support media was characterized by quite good MAE and bias values of 466.53 mg/l of HCO3- and an acceptable value for R2= 0.498. The NMSE was close to 0 with a value of 0.03 and a slightly higher FB= 0.154 than desired. The fuzzy system robustness was tested by adding NaHCO3 to the reactor with the burst cell reticulated polyurethane foam medium and by diluting the reactor where sponge was used as the support media with water. FIS I and FIS II were able to follow the system output closely in the first case, but not in the second. FIS II functionality as an alkalinity predictor was tested through the application on a 28l cylindrical reactor with sponge as the biomass support media treating cow manure. If data that was recorded when severe temperature fluctuations occurred (that highly impact digester performance), are excluded, FIS II performance can be characterized as good by having R2= 0.54 and MAE=Bias= 587 mg/l of HCO3-. Predicted alkalinity values followed observed alkalinity values closely during the days that followed NaHCO3 addition and water dilution. In a second experiment a rulebased and a Mamdani fuzzy logic controller were developed to regulate the organic loading rate based on alkalinity predictions from FIS II. They were tested through the operation of five 6.5l reactors with biomass support media treating cellulose. The performance indices of MAE=763.57 mg/l of HCO3-, Bias= 398.39 mg/l of HCO3-, R2= 0.38 and IA= 0.73 indicate a pretty good correlation between predicted and observed values. However, although both controllers managed to keep alkalinity within the desired levels suggested for stability (>3480 mg/l of HCO3-), the reactors did not reach a stable state suggesting that different loading rates should be applied for biogas systems treating cellulose.New Generation Biogas (NGB

Probabilistic data-driven methods for forecasting, identification and control

This dissertation presents contributions mainly in three different fields: system identification, probabilistic forecasting and stochastic control. Thanks to the concept of dissimilarity and by defining an appropriate dissimilarity function, it is shown that a family of predictors can be obtained. First, a predictor to compute nominal forecastings of a time-series or a dynamical system is presented. The effectiveness of the predictor is shown by means of a numerical example, where daily predictions of a stock index are computed. The obtained results turn out to be better than those obtained with popular machine learning techniques like Neural Networks. Similarly, the aforementioned dissimilarity function can be used to compute conditioned probability distributions. By means of the obtained distributions, interval predictions can be made by using the concept of quantiles. However, in order to do that, it is necessary to integrate the distribution for all the possible values of the output. As this numerical integration process is computationally expensive, an alternate method bypassing the computation of the probability distribution is also proposed. Not only is computationally cheaper but it also allows to compute prediction regions, which are the multivariate version of the interval predictions. Both methods present better results than other baseline approaches in a set of examples, including a stock forecasting example and the prediction of the Lorenz attractor. Furthermore, new methods to obtain models of nonlinear systems by means of input-output data are proposed. Two different model approaches are presented: a local data approach and a kernel-based approach. A kalman filter can be added to improve the quality of the predictions. It is shown that the forecasting performance of the proposed models is better than other machine learning methods in several examples, such as the forecasting of the sunspot number and the R¨ossler attractor. Also, as these models are suitable for Model Predictive Control (MPC), new MPC formulations are proposed. Thanks to the distinctive features of the proposed models, the nonlinear MPC problem can be posed as a simple quadratic programming problem. Finally, by means of a simulation example and a real experiment, it is shown that the controller performs adequately. On the other hand, in the field of stochastic control, several methods to bound the constraint violation rate of any controller under the presence of bounded or unbounded disturbances are presented. These can be used, for example, to tune some hyperparameters of the controller. Some simulation examples are proposed in order to show the functioning of the algorithms. One of these examples considers the management of a data center. Here, an energy-efficient MPC-inspired policy is developed in order to reduce the electricity consumption while keeping the quality of service at acceptable levels