Integrating heat pumps into large-scale electricity-to-heat industrial processes has proven highly successful in enhancing the utilisation of renewable energy and contributing to carbon emission reductions. However, most studies focus on overall system performance, overlooking the detailed thermal behaviour of the heat pump itself. This limits the adaptability of heat pumps in dynamic industrial settings. This work proposes an equation-oriented framework that enables flexible integration of thermodynamically detailed heat pump models into industrial heat recovery systems. A superstructure-based optimisation model is developed to minimise energy costs and enhance efficiency, considering process constraints, network layout, and heat pump performance. The model dynamically optimises heat pump operation and placement to enhance waste heat recovery and overall system integration. Moreover, the approach supports the integration of low-grade utilities to further improve the energy efficiency. The proposed framework is validated through an industrial-scale case study of a crude oil distillation process. Life cycle assessment is conducted to quantify potential environmental and economic benefits. Results show that integrating heat pumps into the system recovered 50.52 % of low-pressure steam, reducing the total operating cost and annual cost by 12.88 % and 12.42 %. Additionally, total net carbon emissions decreased by 28.70 %. Lower electricity prices increase heat pump use and economic benefits but also amplify rebound effects. Furthermore, although high-temperature heat pumps operating above 150 °C tend to increase capital expenditures, they unlock greater energy efficiency, thereby accelerating the industrial decarbonisation process.

Transitioning heat exchanger network (HEN) synthesis designs to industrial application involves operational, environmental, and cost considerations, posing computational challenges. This study proposes a systematic optimization approach integrating multi-objective, multi-period optimization HEN synthesis with waste heat recovery and multiple utilities. The proposed methodology incorporates a novel two-step unit reduction strategy to overcome the increase of model combinational complexities arisen from the multi-period features, thereby facilitating the solving of large-scale problems. Meanwhile, environmental impacts are concerned by using the technique for order preference by similarity to ideal solution approach. A new optimization route, Enhanced Pinch-assisted Multi-Objective Optimization is proposed to obtain the final decision in this multi-objective problem time-efficiently. The case study includes a 15 streams problem, and a real industrial-scale crude oil distillation preheat system. The results showed that assigning carbon compensation to the waste heat recovery option can significantly reduce carbon emissions and change energy distribution.

Building Information Modelling (BIM) plays a key role in the digitisation of the building sector, facilitating the design and construction of buildings. Environmental impacts have become an important factor to consider in building design and construction, often analysed through Life Cycle Assessment (LCA). The integration of BIM and LCA is crucial for supporting sustainable building design and construction. However, there is a lack of up-to-date reviews that consider the role of artificial intelligence (AI) in the integration of BIM and LCA. This paper addresses this gap by examining the recent progress, challenges, and future directions in building carbon emission accounting for buildings. The integration of the BIM-LCA for environmental impact accounting is explored, including goal and scope definition, life cycle inventory, impact assessment, interpretation, interoperability, and integration AI. The results identify gaps in BIM-LCA integration, including transparency issues and reliance on non-local databases. Future directions emphasise enhancing data quality, refining models, and developing AI methods for carbon emission predictions to explore decarbonisation strategy in the building sector. The review contributes to early-stage analysis, facilitating informed decision-making in sustainable building design and construction.

Spiral-wound heat exchangers (SWHEs) offer high heat transfer efficiency and compact design advantages, making them well-suited for services in process industries. Accelerating the application of SWHEs demands design methodologies that avoid extensive user manipulations and complex solution procedures. This study develops a novel incremental-based heat transfer framework for the automated design of single-phase SWHEs, which simultaneously optimizes multi-stream allocation across activated tube layers and exchanger geometries. At each increment, energy balances are enforced for all streams using local heat transfer coefficients and areas. On the tube-side, flow distribution is optimized by permitting variable split heat capacities and mass flow rates within tube layers while ensuring pressure balance for each stream at the bundle outlet. New correlations for shell-side flow regimes are introduced into the proposed sizing model to link discrete tube-layer selections with their corresponding cross-sectional areas throughout the optimization process. The capability of the proposed framework is demonstrated through four case studies, including model validation, two-stream and multi-stream SWHE design, and application to an industrial-scale heat exchanger network (HEN). Rigorous Aspen EDR-CoilWound simulations validate the proposed model and design results, with the HEN case exhibiting only a 2.95 % deviation from the target duty. In Case Study 2, SWHE results in a 24.29 % reduction in required heat transfer area. Case Studies 3 and 4 demonstrate that SWHE configurations can achieve 31.8 %–40.7 % reductions in exchanger volume, attributable to their superior compactness relative to conventional shell-and-tube heat exchangers (STHEs). Benchmarking against detailed STHE designs further clarifies optimal deployment strategies and highlights residual limitations of SWHE technology.

Vertical thermosyphon reboilers (VTRs) are widely used in distillation due to their high heat transfer efficiency and low fouling tendency. However, conventional design approaches and commercial tools often treat two-phase heat transfer, geometry, and pressure balance in isolation and depend on extensive manual tuning, which may yield low solution quality. To address this gap, a rigorous mixed integer nonlinear programming (MINLP) optimization framework is established for VTR design that couples thermodynamic, hydraulic, and geometric decisions simultaneously. A two-stage heat transfer modelling approach is proposed to capture the convective boiling transition by integrating sensible heating and boiling heat transfer mechanisms. Within this approach, a dynamic switching scheme selects heat transfer correlations according to the vapor fraction, which improves accuracy across different operating conditions. Moreover, a comprehensive pressure balance model is established that spans the external piping, the reboiler, and the column sump liquid level to ensure the resulting natural circulation is stable. Geometric and operating variables are optimized simultaneously to deliver coordinated design choices under duty and layout constraints. The optimization is implemented in GAMS/48 and solved using SCIP solver. To demonstrate engineering applicability, the thermodynamic modelling results are compared with Aspen EDR simulations, revealing relative errors ranging from 3.8 % to 15.6 %. The optimized design increases the overall heat transfer coefficient by 16.60 % and reduces the required area by 13.86 %. Furthermore, stability analysis under varying heat duties reveals clear operational boundaries, highlighting the importance of coordinating liquid level and skirt height to maintain feasible natural circulation.