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Processes
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Energy Efficiency, Renewable Integration, and Systematic Energy Management for Decarbonisation...
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Energy Efficiency, Renewable Integration, and Systematic Energy Management for Decarbonisation of Industrial and Agricultural Processes

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 409

Special Issue Editors

Dr. Lorenzo Miserocchi

E-Mail Website
Guest Editor
Department of Energy, Systems, Territory and Construction Engineering, Università di Pisa, Pisa, Italy
Interests: energy engineering; energy efficiency; renewable energy; decarbonization
Prof. Dr. Alessandro Franco

E-Mail Website
Guest Editor
Department of Energy, Systems, Territory and Construction Engineering, Università di Pisa, Pisa, Italy
Interests: thermal engineering; energy efficiency; renewable energy; decarbonization; heat transfer
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The transition toward low-carbon production requires substantial improvements in energy efficiency, together with the integration of renewable energy sources across industrial and agricultural systems. These sectors remain among the most energy-intensive components of modern economies and face increasing pressure to reduce greenhouse gas emissions while maintaining productivity, reliability and economic competitiveness. Addressing these challenges requires coordinated advances in process design, operation and energy management strategies.

This Special Issue focuses on recent developments in energy efficiency, renewable integration and systematic energy management aimed at supporting the decarbonisation of industrial and agricultural processes. Contributions may address methodological developments, analytical frameworks, technological solutions, modelling approaches and case studies that demonstrate measurable improvements in energy and environmental performance.

Relevant topics include, but are not limited to:

  • energy and process optimization;
  • integration of renewable energy;
  • advanced energy management systems;
  • electrification of industrial processes;
  • waste heat recovery;
  • digital monitoring and control;
  • and life-cycle approaches for assessing the environmental implications of energy technologies and operational strategies.

The aim of this Special Issue is to provide a platform for research that advances the understanding, optimisation, and integration of energy systems within industrial and agricultural processes, contributing to the development of more sustainable, resilient, and low-carbon production systems.

Dr. Lorenzo Miserocchi
Prof. Dr. Alessandro Franco
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • energy efficiency
  • renewable energy
  • energy management
  • process optimization
  • electrification
  • waste heat recovery
  • digital monitoring and control
  • industrial decarbonization
  • sustainable agriculture

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Published Papers (1 paper)

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Research

29 pages, 9501 KB  
Article
A Hybrid Mechanistic–AI Framework for Degradation-Aware Energy Analysis and Maintenance-Oriented Decision Support in Bioethanol Production
by Yitong Niu, Natra Joseph, Ireland LaBass, Sicheng Wang, Chee Keong Lee, Cheu Peng Leh and Ting Han
Processes 2026, 14(11), 1806; https://doi.org/10.3390/pr14111806 - 1 Jun 2026
Viewed by 259
Abstract
Bioethanol production from lignocellulosic biomass remains energy-intensive, and its energy performance can be affected by equipment degradation, utility disturbances, and operating variability. This study developed a degradation-aware mechanistic–AI framework for energy forecasting, anomaly detection, maintenance-oriented interpretation, and multi-objective optimization in bioethanol production under [...] Read more.
Bioethanol production from lignocellulosic biomass remains energy-intensive, and its energy performance can be affected by equipment degradation, utility disturbances, and operating variability. This study developed a degradation-aware mechanistic–AI framework for energy forecasting, anomaly detection, maintenance-oriented interpretation, and multi-objective optimization in bioethanol production under limited-data conditions. Reduced-order energy models were formulated for pretreatment, hydrolysis–fermentation, and ethanol purification. Equipment deterioration was represented through heat-transfer fouling, column-efficiency decline, and pump-efficiency decay. Condition-dependent modifiers were introduced to account for load-related degradation and intervention-related partial recovery. Benchmark-constrained synthetic time-series datasets were generated under baseline, accelerated-degradation, condition-dependent, stress, and data-quality perturbation scenarios. Empirical baselines and machine-learning models were compared for specific energy consumption prediction, with uncertainty reported using confidence intervals. The long short-term memory model achieved the lowest prediction errors under both baseline and stress conditions. Robustness testing showed that sensor drift, missing values, and outliers increased forecasting and anomaly-detection uncertainty. Sensitivity analysis identified degradation coefficients, seasonal disturbance, and anomaly-threshold selection as influential factors. Multi-objective optimization revealed trade-offs among specific energy consumption, ethanol purity, and equipment-health penalty. The proposed framework should be interpreted as a benchmarked methodological platform rather than a plant-validated maintenance or control system. Plant-specific deployment requires calibration with operating records, maintenance logs, cleaning records, and sensor-quality assessment. Full article
(This article belongs to the Special Issue Energy Efficiency, Renewable Integration, and Systematic Energy Management for Decarbonisation of Industrial and Agricultural Processes)
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