Model Predictive Control of Thermal Comfort and Indoor Air Quality in livestock stable

Wu, Zhuang; Rajamani, Murali R.; Rawlings, James B.; Stoustrup, Jakob

doi:10.23919/ecc.2007.7068610

Cited by 3 publications

(2 citation statements)

References 14 publications

(19 reference statements)

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“…Basically, the zone division is according to the number of the operating exhaust fans. For a detailed description for developing, simplifying and coupling of the models and significant dynamic parameters estimation, we refer to Wu et al [2005], Wu et al [2007] and Wu et al [2008].…”

Section: Process Dynamic Modelmentioning

confidence: 99%

Moving Horizon Control and Estimation of Livestock Ventilation Systems and Indoor Climate

Stoustrup

Jørgensen

2008

IFAC Proceedings Volumes

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In this paper, a new control strategy involves exploiting actuator redundancy in a multivariable system is developed for rejecting the covariance of the fast frequency disturbances and pursuing optimum energy solution. This strategy enhances the resilience of the control system to disturbances beyond its bandwidth and reduce energy consumption through on-line optimization computation. The moving horizon estimation and control (also called predictive control) technology is applied and simulated. The design is based on a coupled mathematical model which combines the hybrid ventilation system and the associated indoor climate for poultry in barns. The comparative simulation results illustrate the significant potential and advancement of the moving horizon methodologies in estimation and control for nonlinear Multiple Input and Multiple Output system with unknown noise covariance and actuator saturation.

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Section: Process Dynamic Modelmentioning

confidence: 99%

Moving Horizon Control and Estimation of Livestock Ventilation Systems and Indoor Climate

Stoustrup

Jørgensen

2008

IFAC Proceedings Volumes

Self Cite

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“…Over the years, several control approaches for maintaining both IAQ and thermal comfort have been studied but with certain limitations. Among those, few studies implemented model predictive control (MPC) to investigate the combined (i) thermal comfort and CO 2 optimization by regulating fresh air [10], (ii) IAQ (particle concentration) and energy optimization [11], (iii) multi-objective optimization of IAQ (particulate matter) and energy consumption in subway ventilation system [12], (iv) optimization energy and IAQ with focus on CO 2 [13], and (v) energy optimization and air-quality through fresh air induction [3]. More recently, the role of demand controlled ventilation(DCV) on IAQ and energy savings was studied [14].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Ventilation System and Soft-Sensors for Maintaining Indoor Air Quality and Thermal Comfort in Buildings

et al. 2020

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Maintaining both indoor air quality (IAQ) and thermal comfort in buildings along with optimized energy consumption is a challenging problem. This investigation presents a novel design for hybrid ventilation system enabled by predictive control and soft-sensors to achieve both IAQ and thermal comfort by combining predictive control with demand controlled ventilation (DCV). First, we show that the problem of maintaining IAQ, thermal comfort and optimal energy is a multi-objective optimization problem with competing objectives, and a predictive control approach is required to smartly control the system. This leads to many implementation challenges which are addressed by designing a hybrid ventilation scheme supported by predictive control and soft-sensors. The main idea of the hybrid ventilation system is to achieve thermal comfort by varying the ON/OFF times of the air conditioners to maintain the temperature within user-defined bands using a predictive control and IAQ is maintained using Healthbox 3.0, a DCV device. Furthermore, this study also designs soft-sensors by combining the Internet of Things (IoT)-based sensors with deep-learning tools. The hardware realization of the control and IoT prototype is also discussed. The proposed novel hybrid ventilation system and the soft-sensors are demonstrated in a real research laboratory, i.e., Center for Research in Automatic Control Engineering (C-RACE) located at Kalasalingam University, India. Our results show the perceived benefits of hybrid ventilation, predictive control, and soft-sensors.

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A WiFi-enabled indoor air quality monitoring and control system: The design and control experiments

Yang

Zhang

2017

2017 13th IEEE International Conference on Control &Amp; Automation (ICCA)

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Model Predictive Control of Thermal Comfort and Indoor Air Quality in livestock stable

Cited by 3 publications

References 14 publications

Moving Horizon Control and Estimation of Livestock Ventilation Systems and Indoor Climate

Moving Horizon Control and Estimation of Livestock Ventilation Systems and Indoor Climate

Hybrid Ventilation System and Soft-Sensors for Maintaining Indoor Air Quality and Thermal Comfort in Buildings

A WiFi-enabled indoor air quality monitoring and control system: The design and control experiments

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