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        <title>Frontiers in Industrial Engineering | Systems Engineering section | New and Recent Articles</title>
        <link>https://www.frontiersin.org/journals/industrial-engineering/sections/systems-engineering</link>
        <description>RSS Feed for Systems Engineering section in the Frontiers in Industrial Engineering journal | New and Recent Articles</description>
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        <pubDate>2026-08-12T16:32:16.91+00:00</pubDate>
        <ttl>60</ttl>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2026.1779021</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2026.1779021</link>
        <title><![CDATA[Energy and environmental engineering for sustainable industrial systems]]></title>
        <pubdate>2026-06-24T00:00:00Z</pubdate>
        <category>Perspective</category>
        <author>Muhammad Sultan</author>
        <description><![CDATA[The industrial sector is one of the world’s largest energy consumers and a major contributor to greenhouse gas emissions and environmental degradation. The transition to sustainable industrial systems requires a comprehensive transformation of energy consumption, resource flows, waste management, and process intelligence. This perspective study argues that future-ready industrial systems are structured around three interrelated foundational elements: (i) energy and resource efficiency enabled by clean technologies, electrification, and process optimisation; (ii) waste and emissions minimisation through circular, recovery-oriented strategies; and (iii) digital and lifecycle intelligence that supports continuous monitoring, informed decision-making, and system-level optimization. The study synthesizes advances in energy-efficient manufacturing, industrial electrification, circular material flows, waste heat recovery, industrial co-processing, low-carbon process innovations, and Industry 4.0 digitalization to highlight integrated pathways toward industrial sustainability. Persistent implementation challenges are identified, including high capital requirements, retrofit constraints, limited adoption of lifecycle-based decision tools, and weak regulatory and market incentives particularly in developing and emerging economies. To address these barriers, a forward-looking research and deployment agenda is proposed which emphasizes digitally enabled industrial infrastructures, circular and symbiotic industrial ecosystems, integrated pilot and demonstration projects, and energy-economy-environment (3 E) systems modelling. By demonstrating how efficiency, electrification, circularity, and digital intelligence interact within a lifecycle-oriented framework, this perspective provides a roadmap for scalable, resource-efficient, economically viable, and climate-neutral industrial systems.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2026.1769776</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2026.1769776</link>
        <title><![CDATA[A hybrid neural network integrating attention mechanism for time series and non-time series multi-factor electric vehicle energy consumption prediction]]></title>
        <pubdate>2026-02-18T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Wenqiang Zhang</author><author>Ruisheng Chai</author><author>Mingzhe Li</author><author>Yashuang Mu</author><author>Peng Li</author><author>Mitsuo Gen</author>
        <description><![CDATA[IntroductionIn recent years, electric vehicles (EVs) have garnered increasing consumer favor due to their low energy consumption and mechanical simplicity; however, the persistent limitation of short driving range has not been fundamentally resolved and continues to fuel drivers’ range anxiety. To enhance the accuracy of EV energy-consumption prediction, this paper categorizes influencing factors from multiple perspectives and proposes a hybrid neural-network prediction model that integrates temporal features and an attention mechanism.MethodsThe model first partitions the dataset into time-series and non-time-series subsets based on temporal correlation. A convolutional neural network (CNN) is then employed to extract and reconstruct features from the time-series data to reduce computational complexity, after which an attention-enhanced bidirectional gated recurrent unit (AtBiGRU) further captures sequential dependencies. The resulting fitted representations, together with the non-time-series variables, are fed into a deep neural network (DNN) for ensemble learning, yielding precise energy-consumption predictions. By processing sequential and non-sequential data separately, the method effectively improves computational efficiency and model expressiveness.ResultsExperimental results demonstrate that the proposed CNN–AtBiGRU–DNN hybrid model achieves higher prediction accuracy and faster convergence than baseline algorithms.ConclusionThe proposed model validates its effectiveness and advancement in EV energy-consumption prediction.]]></description>
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        <guid isPermaLink="true">https://www.frontiersin.org/articles/10.3389/fieng.2024.1426074</guid>
        <link>https://www.frontiersin.org/articles/10.3389/fieng.2024.1426074</link>
        <title><![CDATA[Semantic-based systems engineering for digitalization of space mission design]]></title>
        <pubdate>2024-08-20T00:00:00Z</pubdate>
        <category>Original Research</category>
        <author>Elaheh Maleki</author><author>Alberto Gonzalez Fernandez</author><author>Nils Fischer</author><author>Quirien Wijnands</author><author>Nikolena Christofi</author>
        <description><![CDATA[The engineering of space systems is a collaborative, iterative process that integrates various domain-specific viewpoints to represent the final system. To ensure consistency across these viewpoints, the European Space Agency (ESA) employs Model-Based System Engineering (MBSE) and Semantic-Based System Engineering (SBSE) methodologies together to improve digital continuity and interoperability across collaborative space system developments. One significant application of semantic engineering in SE is the ESA MBSE Methodology. The ESA MBSE Methodology provides a standardized approach aligned with the European Cooperation for Space Standardization (ECSS), promotes interoperability across MBSE methodologies and tools, and overcomes integration challenges. ESA MBSE Methodology is the input for the Overall Semantic Modeling for Space System Engineering (OSMoSE) which leverages interoperability in the space community. Case studies, such as the EagleEye Earth Observation mission, demonstrate practical applications, highlighting how semantic models enhance efficiency in complex space systems. This paper discusses the importance of semantics and data management in SE and presents a practical solution derived from the ESA MBSE Methodology.]]></description>
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