Abstract

The non-linear, multiphase biochemical dynamics of dry anaerobic digestion (DAD) present significant modeling challenges. While the Anaerobic Digestion Model No. 1 (ADM1) offers comprehensive mechanistic insights, its heavy parameterization limits real-time industrial deployment. Conversely, purely data-driven machine learning models often suffer from physical inconsistency and poor generalization on out-of-distribution industrial data. This paper proposes a novel full-system architecture: a Physics-Guided Hybrid Deep Learning Framework. We integrate a Long Short-Term Memory (LSTM) network featuring a Spatio-Temporal Attention mechanism with an XGBoost ensemble. Crucially, the model is regularized using a physics-informed loss function derived from simplified ADM1 kinetics, ensuring biochemical compliance.

1. Introduction

Anaerobic digestion (AD) is a critical biological process for converting municipal organic waste into renewable biomethane. The process encompasses four cascading biochemical phases: hydrolysis, acidogenesis, acetogenesis, and methanogenesis. In industrial full-scale Dry Anaerobic Digestion (DAD), process instability—primarily driven by volatile fatty acid (VFA) accumulation and subsequent acidification—frequently leads to catastrophic digester failure.

This research formulates a full-scale, production-ready AI system that bridges domain knowledge with advanced deep learning. We introduce a Physics-Guided Spatio-Temporal Attention LSTM (PG-STA-LSTM) coupled with gradient-boosted trees, serving dual purposes: high-fidelity methane yield prediction and virtual soft-sensing of critical stability indicators.

2. System Architecture and Theoretical Framework

2.1 Spatio-Temporal Attention LSTM (STA-LSTM)
Standard LSTMs treat all time steps equally. However, abrupt changes in OLR or pH have delayed but severe impacts. We integrate an attention mechanism to assign dynamic weights to historical states. The context vector $\mathbf{c}_t = \sum_{i=1}^w \alpha_i \mathbf{h}_i$ represents the optimally weighted historical summary, which is then passed through dense layers to predict the methane yield.

2.2 Physics-Informed Loss Function ($L_{PG}$)
To ensure physical consistency, we penalize the neural network if its predictions violate simplified ADM1 conservation laws. Let $\mathcal{F}_{ADM1}(\mathbf{X}_t)$ represent a simplified kinetic constraint bounding the maximum possible methane conversion based on available Volatile Solids (VS).

3. The VFA/ALK Virtual Soft Sensor

Physical measurement of Volatile Fatty Acids (VFA) and Alkalinity (ALK) is costly and requires off-line laboratory titration. The ratio $\gamma_t = \text{VFA}_t / \text{ALK}_t$ is the primary indicator of process stability ($\gamma_t > 0.4$ indicates impending acidification).

By integrating SHAP (SHapley Additive exPlanations), the system provides real-time interpretability, calculating the marginal contribution of parameter $j$ (e.g., Temperature drop) to an alarming VFA/ALK spike.