Predictive Control Strategy for Temperature Control for Milk Pasteurization Process
KeywordModel predictive control; Generic model control; Milk pasteurization process; Temperature control
MetadataShow full item record
AbstractA milk pasteurization process is a nonlinear process and multivariable interacting system. This makes it difficultly to control by the conventional on-off controllers. Even if the on-off controller can managed the milk temperatures in the holding tube and the cooling stage of the plate pasteurizer according to the plant's requirements, the dynamic profiles of the milk temperature are oscillating around a desired value. Consequently, this work is aimed at improving the control performance by a multi-variables control approach with model predictive control (MPC). The proposed algorithm was tested in the case of set point tracking under nominal condition gathered by the real observation. To compare the performance of the MPC controller, a model-based control approach of generic model control (GMC) coupled with cascade control strategy is taken into account. The simulation results demonstrated that a proposed control algorithm performed well in keeping both the milk and water temperatures at the desired set points without any oscillation and overshoot. Because of the predictive control strategy, the control response for MPC was less drastic control action compared to the GMC.
VersionNo full-text in the repository
CitationNiamsuwan S, Kittisupakorn P and Mujtaba IM (2013) Predictive control strategy for temperature control for milk pasteurization process. Computer Aided Chemical Engineering. 32: 109-114.
Link to publisher’s versionhttp://dx.doi.org/10.1016/B978-0-444-63234-0
Showing items related by title, author, creator and subject.
Optimal Multi-Drug Chemotherapy Control Scheme for Cancer Treatment. Design and development of a multi-drug feedback control scheme for optimal chemotherapy treatment for cancer. Evolutionary multi-objective optimisation algorithms were used to achieve the optimal parameters of the controller for effective treatment of cancer with minimum side effects.Hossain, M. Alamgir; Majumder, Md A.A.; Algoul, Saleh (University of BradfordSchool of Computing, Informatics and Media, 2013-01-23)Cancer is a generic term for a large group of diseases where cells of the body lose their normal mechanisms for growth so that they grow in an uncontrolled way. One of the most common treatments of cancer is chemotherapy that aims to kill abnormal proliferating cells; however normal cells and other organs of the patients are also adversely affected. In practice, it¿s often difficult to maintain optimum chemotherapy doses that can maximise the abnormal cell killing as well as reducing side effects. The most chemotherapy drugs used in cancer treatment are toxic agents and usually have narrow therapeutic indices, dose levels in which these drugs significantly kill the cancerous cells are close to the levels which sometime cause harmful toxic side effects. To make the chemotherapeutic treatment effective, optimum drug scheduling is required to balance between the beneficial and toxic side effects of the cancer drugs. Conventional clinical methods very often fail to find drug doses that balance between these two due to their inherent conflicting nature. In this investigation, mathematical models for cancer chemotherapy are used to predict the number of tumour cells and control the tumour growth during treatment. A feedback control method is used so as to maintain certain level of drug concentrations at the tumour sites. Multi-objective Genetic Algorithm (MOGA) is then employed to find suitable solutions where drug resistances and drug concentrations are incorporated with cancer cell killing and toxic effects as design objectives. Several constraints and specific goal values were set for different design objectives in the optimisation process and a wide range of acceptable solutions were obtained trading off among different conflicting objectives. Abstract v In order to develop a multi-objective optimal control model, this study used proportional, integral and derivative (PID) and I-PD (modified PID with Integrator used as series) controllers based on Martin¿s growth model for optimum drug concentration to treat cancer. To the best of our knowledge, this is the first PID/I-PD based optimal chemotherapy control model used to investigate the cancer treatment. It has been observed that some solutions can reduce the cancer cells up to nearly 100% with much lower side effects and drug resistance during the whole period of treatment. The proposed strategy has been extended for more drugs and more design constraints and objectives.
A Connection Admission Control Framework for UMTS based Satellite Systems.An Adaptive Admission Control algorithm with pre-emption control mechanism for unicast and multicast communications in satellite UMTS.Hu, Yim Fun; Halliwell, Rosemary A.; Pillai, Anju (University of BradfordSchool of Engineering, Design and Technology, 2012-11-02)In recent years, there has been an exponential growth in the use of multimedia applications. A satellite system offers great potential for multimedia applications with its ability to broadcast and multicast a large amount of data over a very large area as compared to a terrestrial system. However, the limited transmission capacity along with the dynamically varying channel conditions impedes the delivery of good quality multimedia service in a satellite system which has resulted in research efforts for deriving efficient radio resource management techniques. This issue is addressed in this thesis, where the main emphasis is to design a CAC framework which maximizes the utilization of the scarce radio resources available in the satellite and at the same time increases the performance of the system for a UMTS based satellite system supporting unicast and multicast traffic. The design of the system architecture for a UMTS based satellite system is presented. Based on this architecture, a CAC framework is designed consisting of three different functionalities: the admission control procedure, the retune procedure and the pre-emption procedure. The joint use of these functionalities is proposed to allow the performance of the system to be maintained under congestion. Different algorithms are proposed for different functionalities; an adaptive admission control algorithm, a greedy retune algorithm and three pre-emption algorithms (Greedy, SubSetSum, and Fuzzy). A MATLAB simulation model is developed to study the performance of the proposed CAC framework. A GUI is created to provide the user with the flexibility to configure the system settings before starting a simulation. The configuration settings allow the system to be analysed under different conditions. The performance of the system is measured under different simulation settings such as enabling and disabling of the two functionalities of the CAC framework; retune procedure and the pre-emption procedure. The simulation results indicate the CAC framework as a whole with all the functionalities performs better than the other simulation settings.
Automatic Control Strategies of Mean Arterial Pressure and Cardiac Output. MIMO controllers, PID, internal model control, adaptive model reference, and neural nets are developed to regulate mean arterial pressure and cardiac output using the drugs sodium Nitroprusside and dopamineMahieddine, F.; Enbiya, Saleh A. (University of BradfordSchool of Engineering, Design & Technology, 2013)High blood pressure, also called hypertension is one of the most common worldwide diseases afflicting humans and is a major risk factor for stroke, myocardial infarction, vascular disease, and chronic kidney disease. If blood pressure is controlled and oscillations in the hemodynamic variables are reduced, patients experience fewer complications after surgery. In clinical practice, this is usually achieved using manual drug delivery. Given that different patients have different sensitivity and reaction time to drugs, determining manually the right drug infusion rates may be difficult. This is a problem where automatic drug delivery can provide a solution, especially if it is designed to adapt to variations in the patient’s conditions. This research work presents an investigation into the development of abnormal blood pressure (hypertension) controllers for postoperative patients. Control of the drugs infusion rates is used to simultaneously regulate the hemodynamic variables such as the Mean Arterial Pressure (MAP) and the Cardiac Output (CO) at the desired level. The implementation of optimal control system is very essential to improve the quality of patient care and also to reduce the workload of healthcare staff and costs. Many researchers have conducted studies earlier on modelling and/or control of abnormal blood pressure for postoperative patients. However, there are still many concerns about smooth transition of blood pressure without any side effect. The blood pressure is classified in two categories: high blood pressure (Hypertension) and low blood pressure (Hypotension). The hypertension often occurred after cardiac surgery, and the hypotension occurred during cardiac surgery. To achieve the optimal control solution for these abnormal blood pressures, many methods are proposed, one of the common methods is infusing the drug related to blood pressure to maintain it at the desired level. There are several kinds of vasodilating drugs such as Sodium Nitroprusside (SNP), Dopamine (DPM), Nitro-glycerine (NTG), and so on, which can be used to treat postoperative patients, also used for hypertensive emergencies to keep the blood pressure at safety level. A comparative performance of two types of algorithms has been presented in chapter four. These include the Internal Model Control (IMC), and Proportional-Integral-Derivative (PID) controller. The resulting controllers are implemented, tested and verified for three sensitivity patient response. SNP is used for all three patients’ situation in order to reduce the pressure smoothly and maintain it at the desire level. A Genetic Algorithms (GAs) optimization technique has been implemented to optimise the controllers’ parameters. A set of experiments are presented to demonstrate the merits and capabilities of the control algorithms. The simulation results in chapter four have demonstrated that the performance criteria are satisfied with the IMC, and PID controllers. On the other hand, the settling time for the PID control of all three patients’ response is shorter than the settling time with IMC controller. Using multiple interacting drugs to control both the MAP and CO of patients with different sensitivity to drugs is a challenging task. A Multivariable Model Reference Adaptive Control (MMRAC) algorithm is developed using a two-input, two-output patient model. Because of the difference in patient’s sensitivity to the drug, and in order to cover the wide ranges of patients, Model Reference Adaptive Control (MRAC) has been implemented to obtain the optimal infusion rates of DPM and SNP. This is developed in chapters five and six. Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters, the simulation results have demonstrated that this algorithm cannot cover the wide range of patient’s sensitivity to drugs, due to that shortcoming, a PID controller using a Neural Network that tunes the controller parameters was designed and implemented. The parameters of the PID controller were optimised offline using Matlab genetic algorithm. The proposed Neuro-PID controller has been tested and validated to demonstrate its merits and capabilities compared to the existing approaches to cover wide range of patients.