Gain hands-on experience in process control using control station

Cooper, Douglas J.; Rice, Robert C.; Arbogast, Jeffrey E.

doi:10.23919/acc.2004.1386753

Cited by 4 publications

(5 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Through the IMC‐based tuning process, the K C value was obtained by inputting the τ C value. [ 29,30 ] The τ C parameter will serve to regulate the degree of responsiveness (aggressive, moderate, or conservative) of the PI control system in response to the deviation between the present peak and the TP. Thus, the τ C could be chosen as the following options and accordingly the K C value was calculated.…”

Section: Resultsmentioning

confidence: 99%

A Proportional–Integral Feedback Controlled Automatic Flow Chemistry System to Produce On‐Demand AgAu Alloy Nanoboxes

Bui,

Nguyen,

Dao

et al. 2023

Small Structures

View full text Add to dashboard Cite

Mixed‐metal nanoparticles present a challenging task for synthesis with high precision and reproducibility due to the complex interplay of compositional and reaction condition factors. The present study demonstrates a highly precise and automated flow chemistry technique for synthesizing AgAu alloy nanoboxes with tailored optical properties. The synthesis process involves a proportional–integral (PI) feedback control mechanism that enables accurate regulation of reaction parameters, such as the flow rate, to achieve the target AgAu nanoboxes having a desired UV–vis absorbance wavelength. The PI control algorithm is built on the first‐order plus dead‐time model, which correlates the flow rate of the precursors with the maximum absorbance peaks of the resultant nanoalloy products. Based on the difference between the real‐time measured UV–vis absorbance wavelength and the target wavelength, the flow rate of the precursor (i.e., reagent concentration) is tuned via an iterative process until the real‐time absorbance wavelength of the AgAu alloy nanoboxes is matched with the target setpoint. The implementation of a PI feedback control mechanism in a flow chemistry system can offer a highly versatile and universal strategy for generating on‐demand complex nanomaterials with significantly enhanced consistency and reliability by mitigating concentration variations and minimizing the need for human intervention.

show abstract

Section: Resultsmentioning

confidence: 99%

A Proportional–Integral Feedback Controlled Automatic Flow Chemistry System to Produce On‐Demand AgAu Alloy Nanoboxes

Bui,

Nguyen,

Dao

et al. 2023

Small Structures

View full text Add to dashboard Cite

show abstract

“…The heat exchangers are 1 st order dynamic systems with large time constants. This means that they are thermally inertial systems and can affect overall system performance [5,6]. Some notes are essential about the heat exchanger:…”

Section: System Descriptionmentioning

confidence: 99%

Modeling and fuzzy control of the engine coolant conditioning system in an IC engine test bed

et al. 2010

View full text Add to dashboard Cite

Mechanical and thermodynamical performance of internal combustion engines is significantly affected by the engine working temperature. In an engine test bed, the internal combustion engines are tested in different operating conditions using a dynamometer. It is required that the engine temperature be controlled precisely, particularly in transient states. This precise control can be achieved by an engine coolant conditioning system mainly consisting of a heat exchanger, a control valve, and a controller. In this study, constitutive equations of the system are derived first. These differential equations show the second-order nonlinear time-varying dynamics of the system. The model is validated with the experimental data providing satisfactory results. After presenting the dynamic equations of the system, a fuzzy controller is designed based on our prior knowledge of the system. The fuzzy rules and the membership functions are derived by a trial and error and heuristic method. Because of the nonlinear nature of the system the fuzzy rules are set to satisfy the requirements of the temperature control for different operating conditions of the engine. The performance of the fuzzy controller is compared with a PI one for different transient conditions. The results of the simulation show the better performance of the fuzzy controller. The main advantages of the fuzzy controller are the shorter settling time, smaller overshoot, and improved performance especially in the transient states of the system.

show abstract

“…Para o cálculo e implementação dos parâmetros do GPC existe uma certa complexidade matemática, o que pode inviabilizá-lo em determinadas implementações práticas. Entretanto, a necessidade da elaboração de algoritmos de controle preditivo simples e eficientes visando a programação em plataformas do tipo PLC (Programmable Logic Controller), DSP (Digital Signal Processor), FPGA (Field-Programmable Gate Array) ou em outros aplicativos microcontrolados (que são destaques na indústria) é uma necessidade em chão de fábrica e é evidente nos dias atuais (Cooper, 2004).…”

Section: Introductionunclassified

Implementação e avaliação de algoritmo de controle preditivo embarcado em PLC

Lopes

Coelho

2013

Proceeding Series of the Brazilian Society of Computational and Applied Mathematics

View full text Add to dashboard Cite

Abstract    This paper discusses the application of an advanced control technique, called model-based predictive control (MPC), using a programmable logic controller (PLC) as a digital platform to derive the MPC control algorithm. To reduce the computational cost it is used a simplified approach of the GPC (Generalized Predictive Control) control law, assessed in processes with low-order transfer function. Experimental results with laboratory scale plants are realized to show the effectiveness of the control law, and the embedded predictive controller is evaluated in an application of speed control of hydraulic turbines using an approach denominated hardware in-the-loop.Keywords    Model-based predictive control, Programmable logic controller, Embedded algorithm, Hardware in-the-loop.Resumo    Este trabalho aborda a aplicação de uma técnica de controle avançada, denominada controle preditivo baseado em modelo, utilizando um controlador lógico programável (PLC) como plataforma de desenvolvimento deste algoritmo de controle. Visando uma redução do custo computacional é utilizada uma abordagem simplificada do método GPC -Generalized Predictive Control, válido para processos modelados por funções de transferência de ordem reduzida. Para mostrar a eficácia da técnica são realizados ensaios experimentais com um planta em escala de laboratório e uma simulação hardware in-the-loop utilizando o controlador embarcado no PLC como um regulador de velocidade de turbinas hidráulicas. Palavras-chave   Controle preditivo baseado em modelo, Controlador lógico programável, Algoritmo embarcado, Hardware in-the-loop.

show abstract

Gain hands-on experience in process control using control station

Cited by 4 publications

References 2 publications

A Proportional–Integral Feedback Controlled Automatic Flow Chemistry System to Produce On‐Demand AgAu Alloy Nanoboxes

A Proportional–Integral Feedback Controlled Automatic Flow Chemistry System to Produce On‐Demand AgAu Alloy Nanoboxes

Modeling and fuzzy control of the engine coolant conditioning system in an IC engine test bed

Implementação e avaliação de algoritmo de controle preditivo embarcado em PLC

Contact Info

Product

Resources

About