Decentralized agricultural gasification remains constrained by the thermochemical instability of high-alkali residues, such as straw and stalks. This operational bottleneck is defined by a narrow thermal window: oxidation core temperatures are typically targeted above 1000 °C for effective tar cracking, yet grate temperatures are constrained, often below 850 °C, depending on the specific ash fusion characteristics of the feedstock, to prevent viscous sintering and bed clinkering. This work proposes a conceptual framework for a control strategy designed to address these conflicting requirements through a unified framework integrating inferential soft-sensing, hierarchical Model Predictive Control (MPC), and sensor health monitoring. Machine learning architectures capture temporal dependencies and cumulative thermochemical transformations to reconstruct unobservable internal states. This enables real-time state estimation with reported accuracy levels (average test R2 of 0.91–0.97) and 100% physical consistency through monotonicity constraints, effectively managing the critical thermal lag of densified pellets (400–600 s response time). High-fidelity CFD simulations anchor the soft-sensing layer, ensuring model robustness across the inherent variability of agricultural feedstocks. The architecture shifts control logic from reactive adjustments to anticipatory intervention through adaptive multi-mode operation that decouples high-intensity oxidation from grate integrity limits, while dynamic biochar management serves as a multifunctional control variable for tar cracking enhancement and alkali sequestration. Future work will focus on pilot-scale validation under transient feedstock conditions.
Pocola et al. (Wed,) studied this question.