<abstract xmlns="http://www.w3.org/1999/xhtml">

Glass manufacturing is a high-volume process during which large quantities of natural raw materials are turned into commercial products, with large amounts of non-renewable resources and energy consumed in the process. Therefore, it is critical to support the transition to higher levels of energy and material efficiency, CO2 emissions and increased resource productivity.
The main objective of this paper is to present the results of a simulated correlation study considering the age of the glass furnace, cullet share in the process and batch composition of the glass produced, and their influence on the coefficient of energy consumption. In this work, the quoted dependencies were observed. Identification of relations influencing energy consumption enables optimization of particular technological parameters of the process.
Industrial companies are expected to reduce energy consumption in several ways, including technological improvements in production processes and recover lost energy, and recycling of recyclables from waste. Therefore, studies such as this one allow industrial companies to research and learn from, and implement solutions to meet global regulatory and market expectations.
</abstract>

Glass manufacturing is a high-volume process during which large quantities of natural raw materials are turned into commercial products, with large amounts of non-renewable resources and energy consumed in the process. Therefore, it is critical to support the transition to higher levels of energy and material efficiency, CO2 emissions and increased resource productivity.
The main objective of this paper is to present the results of a simulated correlation study considering the age of the glass furnace, cullet share in the process and batch composition of the glass produced, and their influence on the coefficient of energy consumption. In this work, the quoted dependencies were observed. Identification of relations influencing energy consumption enables optimization of particular technological parameters of the process.
Industrial companies are expected to reduce energy consumption in several ways, including technological improvements in production processes and recover lost energy, and recycling of recyclables from waste. Therefore, studies such as this one allow industrial companies to research and learn from, and implement solutions to meet global regulatory and market expectations.

Influence of Glass Furnace Age, Cullet Share and Glass Color on the Glass Production Energy Efficiency

Architecture, Civil Engineering, Environment

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Glass manufacturing is a high-volume process during which large quantities of natural raw materials are turned into commercial products, with large amounts of...

Glass color	Number of records	Data share, %
Flint	3200	80
Green	400	10
Amber	400	10

Influence of Glass Furnace Age, Cullet Share and Glass Color on the Glass Production Energy Efficiency

Pubblicato online: 04 ago 2022

Pagine: 117 - 123

Ricevuto: 20 set 2021

Accettato: 15 nov 2021

DOI: https://doi.org/10.21307/acee-2021-035

Parole chiave
Energy consumption, Energy efficiency, Energy coefficient, Cullet share, Glass Color, Glass production, Simulation

© 2021 Przemysław DEC et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Dataframe with melting of particular glass colors over several years for the analyzed furnace

Influence of Glass Furnace Age, Cullet Share and Glass Color on the Glass Production Energy Efficiency

Pubblicato online: 04 ago 2022

Pagine: 117 - 123

Ricevuto: 20 set 2021

Accettato: 15 nov 2021

DOI: https://doi.org/10.21307/acee-2021-035

Parole chiaveEnergy consumption, Energy efficiency, Energy coefficient, Cullet share, Glass Color, Glass production, Simulation

© 2021 Przemysław DEC et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Figure 1.

Figure 2.

Figure 3.

Dataframe with melting of particular glass colors over several years for the analyzed furnace

Parole chiave
Energy consumption, Energy efficiency, Energy coefficient, Cullet share, Glass Color, Glass production, Simulation