Model Predictive Control of CMSMPR Crystalliser

One of the most critical components of the chemical industry in terms of crystallisation is the pharmaceutical sector. Most medicine components are expensive and require complex processes for their production, so producing waste is highly inefficient. Another concern is the high-quality standards for most pharmaceutical products. Therefore, optimising the crystallisation process is critical from a quality perspective, with the main concerns being the product's crystal structure and particle diameter distribution. Regardless efficient control in batch processes such as crystallisation is a difficult task due to the inherently nonlinear behaviour of the system. Using a priori model of the system as the basis for nonlinear model predictive control could provide a useful tool for handling the crystallisation process, mitigating the effects of disturbance and noise and ensuring appropriate product quality. In this work, we wish to showcase the possibility of controlling a crystallisation process using model predictive control to enable the production of crystal products with desired particle diameter distribution and crystalline product average size. The method is shown using citric acid as a model substance in a case study of a continuous crystallisation procedure in a stirred tank reactor. The crystalliser model includes an energy balance, so the system's behaviour depends on the cooling rate and residence time. Accordingly, the control problem can be formulated as multiple inputs and multiple outputs (MIMO) system. Moreover, the two controlled (average particle size and crystal size dispersion) variables are not easily detached from each other. So, the traditional controlling strategies, for example, the decoupling controller, is challenging to apply. The MPC (model predictive control) as an advanced control algorithm can be a solution to this

Test Plan for the Verification of the Robustness of Sensors and Automotive Electronic Products Using Scenario-Based Noise Deployment (SND)

The targeted shortening of sensor development requires short and convincing verification tests. The goal of the development of novel verification methods is to avoid or reduce an excessive amount of testing and identify tests that guarantee that the assumed failure will not happen in practice. In this paper, a method is presented that results in the test loads of such a verification. The method starts with the identification of the requirements for the product related to robustness using the precise descriptions of those use case scenarios in which the product is assumed to be working. Based on the logic of the Quality Function Deployment (QFD) method, a step-by-step procedure has been developed to translate the robustness requirements through the change in design parameters, their causing phenomena, the physical quantities as causes of these phenomena, until the test loads of the verification. The developed method is applied to the test plan of an automotive sensor. The method is general and can be used for any parts of a vehicle, including mechanical, electrical and mechatronical ones, such as sensors and actuators. Nonetheless, the method is applicable in a much broader application area, even outside of the automotive industry