Process gases play a crucial role in steelmaking, but various production factors limit their efficiency. Accurate and real-time forecasting of process gas flow is essential for recycling low-caloric gases, minimizing flaring, and enhancing overall energy efficiency. However, traditional forecasting models, such as recurrent statistical approaches, fail to capture the intricate spatiotemporal dependencies of process gases, leading to inaccurate predictions and excessive gas flaring. To address these challenges, we propose a constrained intelligent forecasting framework based on a novel Factorised Dilated Temporal Convolutional Network (FD-TCN). While classical TCNs leverage dilated convolution layers for capturing spatial–temporal information, they suffer from a fixed, task-independent dilation factor that skips important local dynamics of steelmaking processes. FD-TCN mitigates this issue by introducing a factorised dilation mechanism, enabling the efficient expression of diverse temporal patterns. Additionally, existing intelligent gas flow forecasting models seldom enforce system constraints on numerical flow rates, which often produce unrealistic predictions. Our approach integrates domain-specific constraints into the training process, eliminating out-of-range forecasts and improving reliability. We validated FD-TCN across single-variate and multi-variate gas flow forecasting tasks and generalised the method into gas production, gas in manufacturing consumption, and mixed gas recycling. The model achieved the lowest forecasting error and computation usage among the tested architectures. Compared to state-of-the-art deep learning models in process gases utilization, including long-short-term-memory (LSTM) and standard TCN, our constrained FD-TCN method demonstrated 39.7 % higher prediction accuracy in the challenging experiment and achieved a 7.5 × faster prediction time. Constrained FD-TCN presents an efficient, accurate and generalisable gas forecasting solution for energy-efficient industrial processes.