The aim of this study was to analyse the effect of different nitrogen increase rates in feedstock on the process stability and conversion efficiency in anaerobic digestion (AD). The research was conducted in continuously stirred tank reactors (CSTR), initially filled with two different inocula: inocula #1 with low and #2 with high nitrogen (N) concentrations. Three N feeding regimes were investigated: the “0-increase” feeding regime with a constant N amount in feeding and the regimes “0.25-increase” and “0.5-increase” where the N concentrations in feedstock were raised by 0.25 and 0.5 g·kg⁻¹, respectively, related to fresh matter (FM) every second week. The N concentration inside the reactors increased according to the feeding regimes. The levels of inhibition (Inhibition) in specific methane yields (SMY), related to the conversion efficiency of the substrates, were quantified. At the N concentration in digestate of 10.82 ± 0.52 g·kg⁻¹ FM measured in the reactors with inoculum #2 and “0.5-increase” feeding regime, the level of inhibition was equal to 38.99% ± 14.99%. The results show that high nitrogen increase rates in feeding regime are negatively related to the efficiency of the AD process, even if low volatile fatty acid (VFA) concentrations indicate a stable process.

Straws are agricultural residues that can be used to produce biomethane by anaerobic digestion. The methane yield of rice straw is lower than other straws. Steam explosion was investigated as a pretreatment to increase methane production. Pretreatment conditions with varying reaction times (12–30 min) and maximum temperatures (162–240 °C) were applied. The pretreated material was characterized for its composition and thermal and morphological properties. When the steam explosion was performed with a moderate severity parameter of S₀ = 4.1 min, the methane yield was increased by 32% compared to untreated rice straw. This study shows that a harsher pretreatment at S₀ > 4.3 min causes a drastic reduction of methane yield because inert condensation products are formed from hemicelluloses.

In recent decades, an enormous potential of fungal-based products with characteristics equal to, or even outperforming, classic petroleum-derived products has been acknowledged. The production of these new materials uses mycelium, a root-like structure of fungi consisting of a mass of branching, thread-like hyphae. Optimizing the production of mycelium-based materials and fungal growth under technical conditions needs to be further investigated. The main objective of this study was to select fast-growing fungi and identify optimized incubation conditions to obtain a dense mycelium mat in a short time. Further, the influence of the initial substrate characteristics on hyphae expansion was determined. Fungal isolates of Ganoderma lucidum, Pleurotus ostreatus, and Trametes versicolor were cultivated for seven days on substrate mixtures consisting of various proportions of pine bark and cotton fibers. Furthermore, the substrates were mixed with 0, 2, and 5 wt.% calcium carbonate (CaCO₃), and the incubator was flushed with 0, 5, and 10 vol.% carbon dioxide (CO₂). All samples grew in the dark at 26 °C and a relative humidity of 80%. Evaluation of growth rate shows that cotton fiber-rich substrates performed best for all investigated fungi. Although Pleurotus ostreatus and Trametes versicolor showed comparatively high growth rates of up to 5.4 and 5.3 mm d⁻¹, respectively, mycelium density was thin and transparent. Ganoderma lucidum showed a significantly denser mycelium at a maximum growth rate of 3.3 mm d⁻¹ on a cotton fiber-rich substrate (75 wt.%) without CaCO₃ but flushed with 5 vol.% CO₂ during incubation.

Future biogas plants must be able to produce biogas according to demand, which requires proactive feeding management. Therefore, the simulation of biogas production depending on the substrate supply is assumed. Most simulation models are based on the complex Anaerobic Digestion Model No. 1 (ADM1). The ADM1 includes a large number of parameters for all biochemical and physicochemical process steps, which have to be carefully adjusted to represent the conditions of a respective full-scale biogas plant. Due to a deficiency of reliable measurement technology and process monitoring, nearly none of these parameters are available for full-scale plants. The present research investigation shows a simulation model, which is based on the principle of time series analysis and uses only historical data of biogas formation and solid substrate supply, without differentiation of individual substrates. The results of an extensive evaluation of the model over 366 simulations with 48-h horizon show a mean absolute percentage error (MAPE) of 14–18%. The evaluation is based on two different digesters and demonstrated that the model is self-learning and automatically adaptable to the respective application, independent of the substrate’s composition.

Biogas plants have the great advantage that they produce electricity according to demand and can thus compensate for fluctuating production from weather-dependent sources such as wind power and photovoltaics. A prerequisite for flexible biogas plant operation is a suitable feeding strategy for an adjusted conversion of biomass into biogas. This research work is the first to demonstrate a practical, integrated model predictive control (MPC) for load-flexible, demand-orientated biogas production and the results show promising options for practical application on almost all full-scale biogas plants with no or only minor adjustments to the standardly existing measurement technology. Over an experimental period of 36 days, the biogas production of a full-scale plant was adjusted to the predicted electricity demand of a “real-world laboratory”. Results with a mean absolute percentage error (MAPE) of less than 20% when comparing biogas demand and production were consistently obtained.