Energy Efficiency Benchmarking in Industrial Plants: A Structured Review and Framework Synthesis
産業プラントにおけるエネルギー効率ベンチマーキング：構造化レビューとフレームワーク統合 (AI 翻訳)
Angat Jotiram Ghanwat, Avinash. A. Somatkar, Viraj Shailesh Patole, M. Gaikwad
🤖 gxceed AI 要約
日本語
本論文は産業用エネルギー効率ベンチマーキングの構造化レビューを実施し、ISO 50001、Energy Star、PAT等の既存基準・制度を統合したフレームワークを提示。デジタル技術やAIによる予測最適化の可能性にも言及し、ケーススタディを通じてコスト削減と脱炭素化への貢献を示す。中小企業への拡張やネットゼロとの整合性が今後の課題。
English
This structured review synthesizes a framework for industrial energy efficiency benchmarking from standards like ISO 50001, Energy Star, and PAT. It highlights digital technologies and AI for predictive optimization, and case studies demonstrate cost savings and decarbonization benefits. Future work includes extending to SMEs and aligning with net-zero roadmaps.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本の産業界では、省エネ法やトップランナー制度に加え、ISO 50001の認証取得が進んでおり、本レビューが提示するベンチマーキングの枠組みは、企業のエネルギー管理と脱炭素目標設定に直接活用できる。特に、鉄鋼・セメントなどのエネルギー多消費産業において有用。
In the global GX context
Globally, energy efficiency benchmarking is a key strategy for industrial decarbonization. This paper synthesizes best practices from major standards (ISO 50001, Energy Star, PAT) and provides a structured framework that can be adapted by firms and policymakers to align with net-zero targets and improve competitiveness.
👥 読者別の含意
🔬研究者:Provides a synthesized framework and identifies research gaps for benchmarking in SMEs and net-zero alignment.
🏢実務担当者:Offers a step-by-step framework (PDCA, KPIs, data collection) for implementing energy efficiency benchmarking in industrial plants.
🏛政策担当者:Reviews existing benchmarking programs (e.g., PAT, Energy Star) and highlights design elements for effective industrial energy policy.
📄 Abstract（原文）
The global industrial sector is one of the main sources of consumer demands that drive the global economy, but it also represents roughly 37%-40% of the total energy demands worldwide, which makes it the largest contributor to emissions of greenhouse gases. As the price of energy keeps increasing and the environmental regulations are becoming stricter, it has been crucial for companies to set up energy efficiency as one of their top priorities. This study is a structured review of industrial energy-efficiency benchmarking literature, complemented by a synthesized framework derived from existing standards and prior studies. Namely, reviewing standards, such as ISO 50001, the U.S. Environmental Protection Agency-established program "Energy Star", and the Indian scheme Perform, Achieve, and Trade, which is cited by various authors in the scientific literature. The study synthesizes key implementation stages reported in the literature to illustrate how benchmarking programs are commonly structured in industrial practice, which is the essence of the PDCA cycle, thorough data collection and the choosing of the most appropriate key performance indicators are being indicated. This research paper can be seen to comprehend efforts on external benchmarking for this wide range of industrial sector dependent on energy-intensive industries dealing with cement and steel. Besides, the authors depict complicated nature of performance by such technological tools as digital technologies and Artificial Intelligence facilitating on-the-spot, predictive optimization. By examining various case studies from all over the world, the paper is able to show that a well-organized method of benchmarking can bring substantial monetary savings, strengthen the market position, and also be in line with various sustainable development and decarbonization objectives of the industry. Lastly, it also talks about the planned research which is about predictive benchmarking practice, the degree of consonance with net-zero roadmaps while extending these activities to small-and medium-sized enterprises.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.64229/rgmd0771first seen 2026-06-12 05:41:42
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。