The critical challenge of estimating the Remaining Useful Life (RUL) of MoSi2 heating elements utilized in pusher kiln processes is to enhance operational efficiency and minimize downtime in industrial applications. MoSi2 heating elements are integral components in high-temperature environments, playing a pivotal role in achieving optimal thermal performance. However, prolonged exposure to extreme conditions leads to degradation, necessitating precise RUL predictions for proactive maintenance strategies. Since insufficient failure experience deals with Predictive Maintenance (PdM) in real-life scenarios, a Generative Adversarial Network (GAN) generates specific training data as failure experiences. The Remaining Useful Life (RUL) is the duration of the equipment’s operation before repair or replacement, often measured in days, miles, or cycles. Machine learning models are trained using historical data encompassing various operational scenarios and degradation patterns. The RUL prediction model is determined through training, hyperparameter tuning, and comparisons based on the machine-learning model, such as Long Short-Term Memory (LSTM) or Support Vector Regression (SVR). As a result, SVR reflects the actual resistance variation, achieving the R-Square (R²) of 0.634, better than LSTM. From a safety perspective, SVR offers high prediction accuracy and sufficient time to schedule maintenance plans.

To improve traditional cement manufacturing, which generates a large amount of greenhouse gases, blending calcareous green algae and fly ash as cement replacement materials is expected to achieve nearly zero carbon emissions. As a calcareous green alga, Halimeda macroloba is a significant producer of biogenic calcium carbonate (CaCO₃), sequestering approximately 440 kg of carbon dioxide (CO₂) per 1000 kg of CaCO₃, with CaCO₃ production reported in relation to algal biomass. To assess the new low-carbon/low-waste cement production process, the proposed scenarios (2 and 3) are validated via Python-based modeling (Python 3.12) and Aspen Plus^® simulation (Aspen V14). The core technology is the pre-calcination of algae-derived CaCO₃ and fly ash from coal combustion, which are added to a rotary kiln to enhance the proportions of tricalcium silicate (C₃S) and dicalcium silicate (C₂S) for forming the desired silicate phases in clinker. Through the lifecycle assessment (LCA) of all scenarios using SimaPro^® (SimaPro 10.2.0.3), the proposed Scenario 2 achieves the GWP at approximately 0.906 kg CO₂-eq/kg clinker, lower than the conventional cement production process (Scenario 1) by 47%. If coal combustion is replaced by natural gas combustion, the fly ash additive is reduced by 74.5% in the cement replacement materials, but the proposed Scenario 3 achieves the GWP at approximately 0.753 kg CO₂-eq/kg clinker, lower than Scenario 2 by 16.9%. Moreover, the LCA indicators show that Scenario 3 has lower environmental impacts on human health, ecosystem, and resources than Scenario 1 by 24.5%, 60.0% and 68.6%, respectively.