An Integrated Prediction and Optimization Model of a Thermal Energy Production System in a Factory Producing Furniture Components
| Autor: | Halil Akbas, Gultekin Ozdemir |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Mathematical optimization
Control and Optimization Computer science grate-fired boiler 020209 energy Energy Engineering and Power Technology 02 engineering and technology Combustion lcsh:Technology 020401 chemical engineering 0202 electrical engineering electronic engineering information engineering machine learning artificial neural network particle swarm optimization importance analysis thermal energy Production (economics) 0204 chemical engineering Electrical and Electronic Engineering Engineering (miscellaneous) Artificial neural network Renewable Energy Sustainability and the Environment business.industry lcsh:T Particle swarm optimization Factory (object-oriented programming) business Focus (optics) Thermal energy Energy (miscellaneous) |
| Zdroj: | Energies; Volume 13; Issue 22; Pages: 5999 Energies, Vol 13, Iss 5999, p 5999 (2020) |
| ISSN: | 1996-1073 |
| DOI: | 10.3390/en13225999 |
| Popis: | Thermal energy is an important input of furniture components production. A thermal energy production system includes complex, non-linear, and changing combustion processes. The main focus of this article is the maximization of thermal energy production considering the inbuilt complexity of the thermal energy production system in a factory producing furniture components. To achieve this target, a data-driven prediction and optimization model to analyze and improve the performance of a thermal energy production system is implemented. The prediction models are constructed with daily data by using supervised machine learning algorithms. Importance analysis is also applied to select a subset of variables for the prediction models. The modeling accuracy of prediction algorithms is measured with statistical indicators. The most accurate prediction result was obtained using an artificial neural network model for thermal energy production. The integrated prediction and optimization model is designed with artificial neural network and particle swarm optimization models. Both controllable and uncontrollable variables were used as the inputs of the maximization model of thermal energy production. Thermal energy production is increased by 4.24% with respect to the optimal values of controllable variables determined by the integrated optimization model. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |