Sustainable Autoclaved Aerated Concrete Production Strategies Using a Hybrid Discrete-Event Simulation and Machine-Learning Surrogate Framework

Autoclaved Aerated Concrete (AAC) is a lightweight construction material with strong relevance for energy-efficient and modular building systems, but its production remains constrained by steam-curing energy demand and carbon-intensive binders. This challenge is increasingly important as the AAC sector targets net-zero pathways and as cement and lime remain major contributors to life-cycle emissions in AAC products. This study develops an optimization framework for sustainable AAC production that leverages machine-learning surrogates for discrete-event simulations. We first construct a discrete-event factory model that represents mix preparation, mould pouring and rising, cutting, autoclaving, unloading, and product handling. We then couple the simulation to a CO2e and cost model and generate 116,640 production scenarios. Machine-learning surrogate models are trained to predict total CO2e emissions, cost, and production time, and a gradient-based optimization procedure is used to identify operating strategies under different carbon, cost, time, and balanced priorities. The results show that, autoclaving time, electricity carbon intensity, and cement use are the two most important environmental levers. The carbon–cost and carbon-priority strategies produced the lowest predicted emissions, approximately 1292 kg CO2e, and selected the lowest electricity emission factor and cement mass considered in the design space, 0.05 kg CO2e/kWh and 400 kg, respectively. The time-priority strategy produced the shortest predicted production time but the highest predicted emissions and cost, demonstrating a clear carbon–cost–time trade-off under the model assumptions. The proposed framework provides a practical decision-support tool for AAC manufacturers to compare production strategies, quantify trade-offs, and identify lower-carbon operating regimes before implementation.