LAPSE:2023.2380
Published Article

LAPSE:2023.2380
A Novel Graphical Targeting Technique for Optimal Allocation of Biomass Resources
February 21, 2023
Abstract
Biomass has gained global attention as one of the most important renewable energy resources that reduces greenhouse gas emissions. Various research works have been dedicated to biomass supply chain in the past decade as to continuously support the deployment of biomass resources for regional applications. In this work, a novel graphical method based on process integration is proposed for targeting the amount of biomass resources needed for a power generation problem. Apart from having a good visualized interface, the graphical method provides good insights to stakeholders on the macro-level planning of biomass allocation. Two examples are solved to demonstrate the newly proposed methods.
Biomass has gained global attention as one of the most important renewable energy resources that reduces greenhouse gas emissions. Various research works have been dedicated to biomass supply chain in the past decade as to continuously support the deployment of biomass resources for regional applications. In this work, a novel graphical method based on process integration is proposed for targeting the amount of biomass resources needed for a power generation problem. Apart from having a good visualized interface, the graphical method provides good insights to stakeholders on the macro-level planning of biomass allocation. Two examples are solved to demonstrate the newly proposed methods.
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Keywords
bioenergy, composite curves, pinch analysis, power generation, process integration
Subject
Suggested Citation
Foo DCY. A Novel Graphical Targeting Technique for Optimal Allocation of Biomass Resources. (2023). LAPSE:2023.2380
Author Affiliations
Foo DCY: Centre of Excellence for Green Technologies, University of Nottingham Malaysia, Broga Road, Semenyih 43500, Selangor, Malaysia [ORCID]
Journal Name
Processes
Volume
10
Issue
5
First Page
905
Year
2022
Publication Date
2022-05-04
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr10050905, Publication Type: Journal Article
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LAPSE:2023.2380
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https://doi.org/10.3390/pr10050905
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[v1] (Original Submission)
Feb 21, 2023
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Feb 21, 2023
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