An integrated approach to active model adaptation and on-line dynamic optimisation of batch processes

Chen, Tao; Liu, Yi; Chen, Junghui

doi:10.1016/j.jprocont.2013.09.010

Cited by 6 publications

(4 citation statements)

References 33 publications

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“…In the present study, the pattern of adaptation may emerge from two different processes, namely, the community's response (passive adaptation) and the community's role (active adaptation) (Chen et al, 2013). These two forms of adaptation are part of the four development paradigms (Jamaludin, 2016), namely: (1) The growth paradigm (growth paradigm); (2) the Development paradigm of growth and equity (growth and equity strategy development); (3) The paradigm of sustainable development; and (4) development approach paradigm (human development) (Kiely, 2013).…”

Section: Introductionmentioning

confidence: 84%

Cader Phenomena as a Determining Group for the Success of Sustainable Development in Rural Areas

Dulkiah

2023

Khazanah Hukum

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This research examines how the cadre phenomenon as a group determines the success of a sustainable development program in rural areas. This research uses a qualitative approach with descriptive methods. Research data collection was carried out through observation and in-depth interviews with development cadres, government apparatus, community leaders, youth leaders, religious leaders, and the community in the 3 (three) research villages, namely Cidenok Village, Sumberjaya Village, and Garawangi Village, which all three are part of Sumberjaya District, Majalengka Regency, West Java Indonesia. This study found that cadres are one of the determining groups for the success of sustainable development programs in rural areas. The participation of cadres is seen not only in aspects of the government but also in community activities. Cadres who have fast, active, and good adaptation and apply core values can be trusted by the community in disseminating development information and inviting active participation. The success of sustainable development goals (SDGs) in rural areas requires the role of stakeholders both horizontally and vertically between the community and the government. This study concludes that the involvement of cadre groups has a significant role in the successful implementation of the sustainable development program in rural areas.

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Section: Introductionmentioning

confidence: 84%

Cader Phenomena as a Determining Group for the Success of Sustainable Development in Rural Areas

Dulkiah

2023

Khazanah Hukum

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“…Results demonstrate the significant noise suppression capability of DDR-DDST-MFAC, especially in scenarios with increased noise variance, surpassing KF-based methods. Notably, the presence of contaminated Gaussian noise in system output underscores the complexity of noise distributions in real chemical processes, necessitating consideration for future control method development [41][42][43]. Additionally, future efforts will focus on integrating DDR technology with advanced control methods to devise simpler and more efficient control strategies.…”

Section: Discussionmentioning

confidence: 99%

Enhancement of DDST-MFAC for tracking performance by using dynamic data reconciliation

Wang,

Syafiq Sadun,

et al. 2024

Meas. Sci. Technol.

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Model-free adaptive control (MFAC) stands out as an effective data-driven method for addressing nonlinear problems in industrial processes. To maintain good control performance, a data-driven set-point tuning (DDST) method is used to update the virtual set-point of the MFAC system. The DDST-based MFAC (i.e. DDST-MFAC) constantly approaches the target of the process through the nonlinear set-point tuning method. However, due to equipment errors and external interference, industrial sensors often suffer from measurement noise, which can have adverse effects on the control performance. In this article, an available dynamic data reconciliation (DDR) technique is adopted to improve the tracking performance of the DDST-MFAC, which suppresses the impact of process noise by using predicted information and measured data to achieve high-precision requirements for controlling nonlinear processes. Finally, considering both Gaussian and non-Gaussian distribution of measurement noise, the effectiveness of the proposed method was verified through the simulation of a nonlinear nonaffine plant. It is also applied to the steam-water heat exchange process, the control result is improved ultimately.

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“…The coefficient may decrease when the bacteria progress from the exponential growth phase to the stationary phase, as shown in the plot at time 60 hours to 80 hours. In addition, considering the control problems of batch processes that may exist in the cases of expectation setting, repetitive operation, and under incomplete process information, further research directions include parameter optimization for data-driven setting tuning MFAC [35], data-driven control in a two-dimensional framework [36], control methods under incomplete information [37], control methods based on active learning [38] dynamic data reconciliation [39], etc.…”

Section: Fermentation Process Of Pso-mfac Controllermentioning

confidence: 99%

Particle Swarm Optimization-Based Model-Free Adaptive Control for Time-Varying Batch Processes

Wang,

Sadun,

Jalaludin

et al. 2024

Int. J. Automot. Mech. Eng.

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The batch process is a production process with strong nonlinearity, which usually suffers from time-varying parameters and uncertainty of disturbances. Concerning the mentioned problems, this study proposes to investigate the application of the particle swarm optimization-based model-free adaptive control (PSO-MFAC) method for time-varying batch processes. Model-Free Adaptive Control (MFAC) is a data-driven control method, which is one of the promising methods to solve the nonlinear process. Firstly, a Full Form Dynamic Linearization Model-Free Adaptive Control method has been adopted for the control of batch processes. Further, considering that the adopted model-free adaptive control involves seven control parameters, such as cognitive scaling factor (φ1), social scaling factor (φ2), inertia weight (φ3), learning rate (η), control parameter update rate, exploration rate and learning rate for MFAC obtained by a particle swarm optimization (PSO) algorithm in combination with a criterion function performance index. Finally, by comparing it with the existing methods, a typical batch fermentation was applied to verify that PSO-MFAC had a good control effect. The findings indicate that the PSO-MFAC controller exhibits a preference for exploiting the optimal option due to its φ3 value less than 0.1. The efficacy and feasibility of the PSO-MFAC control effect have been proven by obtaining the lowest integral square error (ISE) value of 1.1192 regarding the nonlinearity of the batch process due to time-varying challenges.

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An integrated approach to active model adaptation and on-line dynamic optimisation of batch processes

Cited by 6 publications

References 33 publications

Cader Phenomena as a Determining Group for the Success of Sustainable Development in Rural Areas

Cader Phenomena as a Determining Group for the Success of Sustainable Development in Rural Areas

Enhancement of DDST-MFAC for tracking performance by using dynamic data reconciliation

Particle Swarm Optimization-Based Model-Free Adaptive Control for Time-Varying Batch Processes

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