Thermal Performance Evaluation of an Induced Draft Evaporative Cooling System through Adaptive Neuro-Fuzzy Interference System (ANFIS) Model and Mathematical Model

Baetens, Jens; Eetvelde, Greet Van; Lemmens, Gert; Kayedpour, Nezmin; Kooning, Jeroen D. M. De; Vandevelde, Lieven

doi:10.3390/en12132544

Cited by 7 publications

(8 citation statements)

References 14 publications

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“…A white-box model for the induced draft cooling tower based on first principles is proposed in [6]. A comparison of this white-box model and a black-box model based on an adaptive neuro-fuzzy interference system (i.e., a neural network trained with an interference system based on fuzzy rules) in [13] shows that white-box models are superior for identifying basin temperature in the induced draft cooling tower. However, white-box models are computationally expensive and challenging to implement in a real-world setup.…”

Section: B Evaporative Cooling System (Ecs)mentioning

confidence: 99%

“…However, white-box models are computationally expensive and challenging to implement in a real-world setup. In this paper, we aim to overcome these challenges by defining grey-box physics informed networks (PhyNN and PhyLSTM) that (i) are computationally less expensive than the white-box models defined in [6], and (ii) respect the physical laws of the system, as opposed to the model defined in [13].…”

Section: B Evaporative Cooling System (Ecs)mentioning

confidence: 99%

“…Different methods can be adopted to model the basin temperature based on the fans' power, including mathematical models and data-driven models. In this section, a white-box model of the induced draft cooling tower based on previous research [13] is extended to estimate the operational flexibility of the process.…”

Section: Induced Draft Cooling Towermentioning

confidence: 99%

See 2 more Smart Citations

Physics-Informed LSTM Network for Flexibility Identification in Evaporative Cooling System

Lahariya

Karami

Develder

et al. 2023

IEEE Trans. Ind. Inf.

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In energy intensive industrial systems, an evaporative cooling process may introduce operational flexibility. Such flexibility refers to a system's ability to deviate from its scheduled energy consumption. Identifying the flexibility, and therefore, designing control that ensures efficient and reliable operation presents a great challenge due to the inherently complex dynamics of industrial systems. Recently, machine learning models have attracted attention for identifying flexibility, due to their ability to model complex nonlinear behavior. This research presents machine learning based methods that integrate system dynamics into the machine learning models (e.g., Neural Networks) for better adherence to physical constraints. We define and evaluate physics informed long-short term memory networks (PhyLSTM) and physics informed neural networks (PhyNN) for the identification of flexibility in the evaporative cooling process. These physics informed networks approximate the time-dependent relationship between control input and system response while enforcing the dynamics of the process in the neural network architecture. Our proposed PhyLSTM provides less than 2% system response estimation error, converges in less than half iterations compared to a baseline Neural Network (NN), and accurately estimates the defined flexibility metrics. We include a detailed analysis of the impact of training data size on the performance and optimization of our proposed models.

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Section: B Evaporative Cooling System (Ecs)mentioning

confidence: 99%

Section: B Evaporative Cooling System (Ecs)mentioning

confidence: 99%

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Physics-Informed LSTM Network for Flexibility Identification in Evaporative Cooling System

Lahariya

Karami

Develder

et al. 2023

IEEE Trans. Ind. Inf.

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“…Utility processes in industry with demand side response framework into the energy grid are good examples where dynamic modelling is relevant for CACS optimization. The safety aspects of such processes are discussed in [87] from a thermal objective perspective.…”

Section: Smart Resource Managementmentioning

confidence: 99%

Context Aware Control Systems: An Engineering Applications Perspective

et al. 2020

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Cyber-physical systems revolve around context awareness, empowering objective-oriented services, products and operations based on real data. Self-aware and self-control systems are core elements in the Industry 4.0 framework towards self-sustainable adaptive manufacturing and personalized services. This development is witnessed by the context-aware pervasive assistance to users and machines in decisions making process for optimizing product performance and economic yield. While integration of the virtual and the physical world entails smart sensors communication and complex data analytics, it relies on artificial intelligence tools to manage process operations. The objective of the article is to create awareness that systems & control community must address theoretical and practical aspects from a larger perspective. Context aware control is emerging as a natural solution to maximize the use of available sensing instrumentation and the relatively low cost data logging, i.e. an important source for extracting information, interpreting and using context information and adapt its functionality to the current context of use. This article presents a concise overview of applications where context aware systems and control methodologies are relevant in the seven societal challenges acknowledged by European policy-makers: Digital Society;

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“…Such black-box models however lack the physical understanding of the system and can result in outputs that are physically improbable as they fail to extrapolate well. A good example is the use of an adaptive neuro-fuzzy inference for predicting basin temperature in an evaporate cooling tower [4], where it is noted that white-box models tend to perform better. However, these black-box models have been studied for specific industrial applications and not for the generic buffer formulation.…”

Section: Introductionmentioning

confidence: 99%

Physics-informed Recurrent Neural Networks for The Identification of a Generic Energy Buffer System

Lahariya

Karami

Develder

et al. 2021

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)

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Energy storage is ubiquitous in industrial processes and comes in many forms such as material, chemical, electromechanical buffers. System identification of such energy buffers demands proper estimation/prediction of their physical quantities and unknown parameters. Once these parameters are determined, the identified model can be employed to predict the industrial process dynamics, which finally assist to build efficient control for these processes. This paper proposes physics-informed neural networks-based grey-box modeling methods for the identification of energy buffers. The underlying system dynamics are enforced on the neural network structure to ensure that the identified grey-box model follows the approximate physics. We define two novel grey-box models based on simple and recurrent neural network architectures and test these models for a generic energy buffer. Performance and training time for the proposed grey-box models are compared against a black-box baseline model. Results confirm that imposing the dynamic system's physics on the network improves the performance, and utilizing a recurrent architecture leads to a further improvement.

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Thermal Performance Evaluation of an Induced Draft Evaporative Cooling System through Adaptive Neuro-Fuzzy Interference System (ANFIS) Model and Mathematical Model

Cited by 7 publications

References 14 publications

Physics-Informed LSTM Network for Flexibility Identification in Evaporative Cooling System

Physics-Informed LSTM Network for Flexibility Identification in Evaporative Cooling System

Context Aware Control Systems: An Engineering Applications Perspective

Physics-informed Recurrent Neural Networks for The Identification of a Generic Energy Buffer System

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