Data-driven framework for boiler performance monitoring

Nikula, Riku-Pekka; Ruusunen, Mika; Leiviskä, Kauko

doi:10.1016/j.apenergy.2016.09.072

Cited by 42 publications

(19 citation statements)

References 38 publications

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“…The methodology is developed following an empirical approach, on the basis of needs and requirements identified through a close collaboration with industrial practitioners involved in the case study and afferent to the different functions, such as energy, operations and maintenance management. Some of the tools integrated in the methodology adopt features from similar non-energy-related tools already in use, such as quality and process control tools as well as project management tools [9,10,27,28,76,77], in order to overcome implementation barriers mainly related to usability and resistance to change. As a result, people involved in the case study are able to work with familiar tools, even though based on new data analysis and real time control concepts and applied to the energy management context.…”

Section: Methodsmentioning

confidence: 99%

“…The third step is the energy baseline method definition. Among the several methods available to analyze data and to build the energy baseline, we chose statistical regression [25,27,81,82] as the most effective to organize the data collection accordingly. However, physical models, artificial neural networks or even machine learning techniques [15,28] are employable.…”

Section: Methodology For Real Time Energy Performance Controlmentioning

confidence: 99%

“…However, various techniques might be used to characterize the baseline energy performance of the system, depending on its complexity and on the availability and quality of data (e.g., air compressors, boilers, pumps, buildings, etc.) [27][28][29][30][31][32][33]. Often, for this purpose, statistical regression is used, and more recently-machine learning methodologies, such as artificial neural networks or support vector regression; all of which have been tested.…”

Section: Introductionmentioning

confidence: 99%

“…The most common approach is to evaluate the residual between actual energy consumption and the prediction obtained from the baseline model. In order to help the evaluation of the residuals' significance, control charts, such as EWMA (exponentially weighted moving average), CuSum (cumulative sum) or Shewhart charts are used [27,28,30].…”

Section: Introductionmentioning

confidence: 99%

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Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications

et al. 2019

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Most manufacturing and process industries require compressed air to such an extent that in Europe, for instance, about 10% of the total electrical energy consumption of industries is due to compressed air systems (CAS). However, energy efficiency in compressed air production and handling is often ignored or underestimated, mainly because of the lack of awareness about its energy consumption, caused by the absence of proper measurements on CAS in most industrial plants. Therefore, any effective energy saving intervention on generation, distribution and transformation of compressed air requires proper energy information management. In this paper we demonstrate the importance of monitoring and controlling energy performance in compressed air generation and use, to enable energy saving practices, to enhance the outcomes of energy management projects, and to obtain additional benefits for non-energy-related activities, such as operations, maintenance management and energy accounting. In particular, we propose a novel methodology based on measured data, and baseline definition through statistical modelling and control charts. The proposed methodology is tested on a real compressed air system of a pharmaceutical manufacturing plant in order to verify its effectiveness and applicability.

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Section: Methodsmentioning

confidence: 99%

Section: Methodology For Real Time Energy Performance Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications

et al. 2019

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“…The quantification of this energy saving potential based on the energy performance indicator are very useful, an example of this is the formulation of methods for the estimation of exergetic loss and the exergetic efficiency of the boilers [3], another case is the evaluation of the performance of a boiler in an ethanol production plant by means of exergy and irreversibility analysis where the individual components of the system are evaluated [4]. Other investigations are oriented to determinate performance evaluation of the actual efficiency in the boiler, being able to present estimates of the real efficiency and the expected efficiency, having with based the set of historical data of the equipment [5]. On the other hand, some investigations estimate the uncertainty in the measurements intended to determine the thermal equilibrium of a coal boiler by different analytical method allowing to simulate the system [6].…”

Section: Introductionmentioning

confidence: 99%

Energy saving potential for industrial steam boiler

Escorcia¹,

Ochoa²,

Avella³

2017

ces

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This paper presents the methodology and results of applying a performance analysis of a 75,000-pound capacity / hour capacity boiler integrated into the energy management system for energy planning in a company, in order to identify the potentials of energy saving. The fundamental data used to estimate the energy performance indicators, a brief description of the equipment characteristics and the results of the quantitative analysis of the process are presented. In addition, the methodology proposed for this study is based on the use of the tools of an energy management system, using the stages that carry this procedure, these stages allow the improvement the energy consumption of the company. The study showed a linear correlation R 2 = 0.9615 and a target line of the form Et = 73.147P + 49.061 with a linear correlation R 2 = 0.9782 and a saving potential by good manufacturing practices, which are not associated with production of 6.52%, which shows that although there are good operating practices.Keywords: Steam Boiler, energy management system, energy performance indicators 902 Yulineth Cárdenas Escorcia et al.

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Estimation and visualization of process states using latent variable models based on Gaussian process

Kaneko

2021

Analytical Science Advances

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The estimation and visualization of process states are important for process control in chemical and industrial plants. Since industrial processes are related to Gaussian distributions theoretically, this study focused on Gaussian process latent variable models. Process state estimation and visualization methods are proposed using two latent variables based on the Bayesian Gaussian process latent variable model (BGPLVM), infinite warped mixture model (iWMM), and Gaussian process dynamical models (GPDM). The Tennessee Eastman process dataset was analyzed and it was confirmed that the performance of estimating the process states was highest in the order of GPDM, iWMM, and BGPLVM. Moreover, time‐delayed process variables were added to the process variables to consider the process dynamics, which further improved the performance of estimating the process states. Particularly in the case of GPDM, only two latent variables could estimate the process states, with approximately 100% accuracy for four process states. Additionally, even 10 process states could be estimated with approximately 90% accuracy, and it was confirmed that the process state estimation and process state visualization could be achieved simultaneously.

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Data-driven framework for boiler performance monitoring

Cited by 42 publications

References 38 publications

Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications

Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications

Energy saving potential for industrial steam boiler

Estimation and visualization of process states using latent variable models based on Gaussian process

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