Energy and Resource Efficiency in the Process Industries

Krämer, Stefan; Engell, Sebastian

doi:10.1002/9783527804153.ch1

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…Production Process Efficiency: Minimizing Environmental Impact at the Source Efficient production processes play a crucial role in minimizing environmental impact at the source. By focusing on resource efficiency, companies can reduce their ecological footprint and conserve natural resources (Kra mer & Engell, 2018). This can be achieved through strategies such as optimizing production processes, implementing waste reduction measures, and promoting the use of renewable materials (Patel et al, 2022).…”

Section: Mitigating Greenhouse Gas Emissions In the Supply Chain: A M...mentioning

confidence: 99%

Sustainability in Supply Chains: Strategies and Practices for a Greener Future

Makprang

2024

GBAFR

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Purpose – The purpose of this study is to investigate the critical importance of integrating sustainability practices into supply chain management. It aims to explore strategies and best practices that businesses can adopt to mitigate environmental challenges, reduce greenhouse gas emissions, and transition towards greener supply chains.Implications – The integration of sustainability into supply chain management has far-reaching implications for businesses, the environment, and society. By adopting sustainable practices, organizations can enhance their brand reputation, increase customer loyalty, achieve cost savings, improve risk management, foster innovation, and gain a competitive advantage. Additionally, the adoption of green supply chain practices contributes to environmental conservation, resource efficiency, and the mitigation of climate change impacts.Originality/Value – This study offers a comprehensive analysis of sustainable supply chain practices, with a particular focus on Thailand's manufacturing sector. It provides valuable insights into the challenges and opportunities associated with implementing sustainability strategies across various industries. The research highlights best practices, case studies, and recommendations tailored to the Thai context, enabling businesses to navigate the transition towards greener supply chains effectively. Furthermore, the study emphasizes the significance of Thailand's sustainability efforts in influencing global standards and positioning the nation as a leader in responsible manufacturing practices.

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Section: Mitigating Greenhouse Gas Emissions In the Supply Chain: A M...mentioning

confidence: 99%

Sustainability in Supply Chains: Strategies and Practices for a Greener Future

Makprang

2024

GBAFR

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“…Therefore, the energy performance indicator (EnPI) must eliminate non-influenceable aspects like outside temperatures or economic-driven fluctuating plant loads in order to verify improvement measures. New approaches using context models and data-driven surrogate models representing normalized EnPIs baselines give an answer to these tasks [2].…”

Section: Surrogate Models For Energy Performance Analysismentioning

confidence: 99%

Tool Chain to Extract and Contextualize Process Data for AI Applications

Sherpa

Müller-Pfefferkorn

Enste³

et al. 2023

Chemie Ingenieur Technik

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Summarizing a key use case of a research workstream of the German publicly funded KEEN project, methods and tool chains are demonstrated to extract and to contextualize process data in an automated way based on engineering information. The contextualized process data serves as a high-quality data source for machine learning methods. The article covers the applied basic methodical approaches, design decisions and the results of a successful pilot installation of the developed tool chain.

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“…T N known inputs , any ML approach (e.g., artificial neural networks [4], canonical partial least squares [13], support vector machines [14], etc.) can be, in principle, a good candidate to build plant surrogate models in the general form = ; , , , regression parameters, (1) or submodels (equations being part of a larger model) relating some variables…”

Section: Prediction Models and Constrained Regressionmentioning

confidence: 99%

“…However, in the process industries (those that process bulk materials or resources to transform them into products), these expected advances will not come alone by just collecting huge amounts of data and presenting them in a nice view: data treatment and analytics is necessary to ensure the data quality. Moreover, models for reliable predictions need to be built upon such data, in order to be later used in advanced control, optimization and planning routines [1].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and the Digital Era from a Process Systems Engineering Perspective

Pitarch¹,

Prada²

2022

SNE

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Modern sensorization, communication and computational technologies provide collecting and storing huge amounts of raw data from large cyber-physical systems. These data should serve as the basis to take better decisions at all levels (from the design to operation and management). Nevertheless, raw data need to be transformed in useful information, usually in the form of prediction models. Machine learning plays a key role in this task. Process industry is not alien to this digital transformation, although large processing plants present particularities that differentiate them from other systems. These differences, if neglected, can make machine learning for general purpose fail in extracting the right information from data, leading thus to unreliable process models. As such models are the basis on which the ideas towards the cognitive plant rely, this issue is of major importance for a successful full digitalization of the process industry. In this paper the authors discuss these aspects, as well as some suitable machine-learning approaches, through their experience gained from applying advanced engineering in an industrial case study.

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Energy and Resource Efficiency in the Process Industries

Cited by 4 publications

References 11 publications

Sustainability in Supply Chains: Strategies and Practices for a Greener Future

Sustainability in Supply Chains: Strategies and Practices for a Greener Future

Tool Chain to Extract and Contextualize Process Data for AI Applications

Machine Learning and the Digital Era from a Process Systems Engineering Perspective

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