Anaerobic digestion (AD) is a promising way to produce renewable energy. The solid-state anaerobic digestion (SSAD) with a dry matter content more than 15% in the reactors is seeing its increasing potential in biogas plant deployment. The relevant processes involve multiple of evolving chemical and physical phenomena that are not crucial to conventional liquid-state anaerobic digestion processes (LSAD). A good simulation of SSAD is of great importance to better control and operate the reactors. The modeling of SSAD reactors could be realized either by theoretical or statistical approaches. Both have been studied to a certain extent but are still not sound. This paper introduces the existing mathematical tools for SSAD simulation using theoretical, empirical and advanced statistical approaches and gives a critical review on each type of model. The issues of parameter identifiability, preference of modeling approaches, multiscale simulations, sensibility analysis, particularity of SSAD operations and global lack of knowledge in SSAD media evolution were discussed. The authors call for a stronger collaboration of multidisciplinary research in order to further developing the numeric simulation tools for SSAD.

This study aimed to define the optimal composition of three heterogeneous substrates of the anaerobic digestion process to maximize methane production. The investigated substrates were sewage sludge (SS), the organic fraction of municipal solid waste (OFMSW), and horse waste (HW). The optimal composition of these substrates was defined using the mixture design and, more specifically, the simplex–centroid mixture design. Customized methods and materials were employed to study the complex mixture design of these substrates. The findings revealed that the optimal mixture involved all three substrates with the composition 0.17 HW, 0.66 SS, and 0.17 OFMSW, which demonstrated the highest methane yield at 269 NmL·g_VS⁻¹. In addition, a mathematical model was developed to predict methane production based on a specific composition of co-substrates. The results were validated at the small pilot scale.

Solid-state anaerobic digestion (SS-AD) is a promising technology for treating organic waste and producing renewable energy. This study explores the feasibility of using 3D printing to rapidly design cost-effective laboratory-scale digesters for optimization experiments. Batch reactors were designed using fused deposition modeling (FDM) with polylactic acid (PLA) and stereolithography (SLA) with High Temp V2 resin. PLA had a negligible impact on methane yields, while raw SLA resin positively influenced methanogenic potential, likely due to residual isopropanol used in post-processing, causing a 19% increase in CH4 yield. The performance of the 3D-printed reactors was compared to that of a conventionally machined PMMA reactor using cattle manure as a substrate, showing comparable methane yields and process stability. Three-dimensional printing technologies have demonstrated remarkable efficiency in designing laboratory-scale digesters, with a 70% cost reduction for SLA technology and an 80% reduction in design time compared to conventional reactors designed by plastics processing, while maintaining comparable biogas production. FDM technologies with PLA have shown that they are not suitable for these uses. This study demonstrates the potential of additive manufacturing to accelerate SS-AD research and development. However, care must be taken in material selection and post-processing to avoid introducing experimental bias.

River water quality (RWQ) monitoring faces growing challenges from geographical data gaps, methodological inconsistencies, and limited uncertainty quantification in predictive modelling frameworks. This systematic review aims to critically synthesise, compare, and identify knowledge gaps in AI-driven RWQ prediction studies, by synthesizing 71 peer-reviewed case studies published between 2018 and 2024, grouped into (i) traditional water quality index (WQI) approaches (n = 44) and (ii) remote sensing (RS) approaches (n = 27), both employing machine learning or deep learning (ML/DL) methods. A strong geographical bias is identified, with 83% of studies concentrated in Asia, and limited representation from other regions. The review identified that WQI studies exhibit substantial variability in parameter selection, sampling type and frequency, weighting, and aggregation. RS-based studies predominantly retrieve optically active (OA) parameters using Sentinel-2 and Landsat-8/9 imagery, with optically inactive (OIA) parameters inferred indirectly. Across both groups, performance evaluation relies largely on conventional accuracy metrics, with sensitivity and uncertainty analyses rarely reported. Overall, this review identifies inconsistent preprocessing protocols, and the absence of standardised benchmarking as critical barriers to the transferability and operational reliability of current RWQ prediction frameworks. This review provides a consolidated evidence base and practical recommendations for developing more transferable and operationally reliable RWQ prediction framework with priority directions for future research.

In the last decades, the growing awareness of climate change and the high political sensitivity of critical resources such as energy have emphasized a need for local, renewable and optimized energy mixes at various scales. Several studies have therefore aimed to optimize renewable energy technologies and plant locations to develop more renewable and efficient Energy Mixes. Following this trend, this paper applies and compares Goal Programming, Branch-and-Cut and NSGA-II to a multi-objective combinatorial optimization problem focused on the energy mix of Oise, France. Results show more optimality for Goal Programming and Branch-and-Cut, accompanied by a high sensitivity to constraints, while NSGA-II provides more technological diversity in the computed solutions.