Biogas, generated from small scale digesters, is a traditional energy source for satisfying the thermal energy demand in off-grid communities. Recent developments in small scale solid oxide fuel cells (SOFCs) technology and progress in research and development of dry reforming, opens perspectives to couple small scale SOFCs with already existing digesters to meet both thermal and electrical energy demand, enabling power access to off-grid communities. However, one of the major challenges for SOFC integration to small scale digesters is the effect of biogas impurities, such as H₂S, on the performance of SOFCs. Previous work has shown that local operational practices could influence the biogas quality and particularly the H₂S content in the biogas. The here presented research expanded on the use of cow urine instead of water as solvent in manure digestion as a potential operational strategy that enables in-situ reduction of H₂S in the evolving biogas. This research investigated the following hypotheses: 1) urine addition results in a high pH that favours HS⁻ over H₂S, 2) given the presence of metal elements in the cow urine, insoluble metal sulphides are being formed, reducing the biogas H₂S content. The research was carried out by measuring cow urine composition of various samples, assessing the effects of different urine/water/manure mixtures on the evolving biogas-H₂S concentration, and verifying the experimental findings with phreeqC equilibrium speciation. Bio-kinetic modelling, using the anaerobic digestion model nr 1 (ADM1), was subsequently performed to explore the influence of different feed compositions on the H₂S content in the biogas. It was observed that addition of cow urine in all experiments resulted in an elevated pH of the reactor compared to water dilution, yet both experiments I and II-2 showed an increased maximum H₂S content when urine dilution was applied, compared to water dilution. Cow urine and cow dung characterisation in terms of metals and S, showed that experiment II-1 had the highest Fe:S ratio of 1:0.3–1:0.9. Equilibrium modelling confirmed that despite the positive urine-induced pH effect, the measured Fe:S ratios could indeed be decisive, as with an Fe;S ratio of 1:6 and 1:0.5, the H₂S production at equilibrium was 61 and 10 mL/ kg of solution, respectively. Furthermore, it was predicted through bio-kinetic modelling that inconsistency in feedstock composition may result in temporary H₂S peaks exceeding 400 ppm. Overall, results showed that if a cow urine/manure mixture is characterised by a total metal:S ratio exceeding 1:0.5 and total S content of less than 0.5 mM, then hydrolysed cow urine addition presents an interesting in-situ H₂S cleaning strategy for biogas-SOFC applications.

Coffee processing encompasses the conversion of coffee cherries into marketable products, including the removal of outer layers to produce green coffee and, in extended chains, their roasting into roasted coffee, and grinding into ground coffee. Calculating the carbon footprint (CF) in coffee processing is crucial for identifying and mitigating key sources of greenhouse gas (GHG) emissions. Utilizing the Life Cycle Assessment (LCA) methodology, the current study quantifies the CF associated with Robusta dry coffee processing by collecting primary data through interviews with coffee producers and visits to coffee processing units, roasting, and grinding facilities in Wayanad, India. The study identifies GHG emission hotspots across two scenarios. Scenario A includes transportation of dried coffee beans from farm to coffee processing unit, green coffee production, packaging, roasting, and grinding at a local unit, while Scenario B covers local transportation of green coffee beans from India to The Netherlands, green coffee production, packaging, and its transportation from India to The Netherlands. Cultivation and harvesting of coffee cherries, consumer-level preparation and use, and disposal of coffee products are outside the scope of this study. The functional unit is defined as 1 kg of green coffee for both scenarios. Findings show that the CF equals 0.62 and 0.38 kg CO_2eq per kg of green coffee for scenarios A and B, respectively. Roasting (78 % of CF), and sea transportation (66 % of CF) emerged as the main hotspots of GHG emissions for scenario A, and scenario B, respectively.

Temperature plays a critical role in performance and stability of anaerobic digestion processes, subject to frequent meteorological fluctuations. However, state-of-the-art modeling and process control approaches for anaerobic digestion often do not consider the temporal dynamics of the temperature, which can influence microbial communities, kinetics, and chemical equilibrium, and consequently, biogas production efficiency. Therefore, to account for anaerobic digesters operating under fluctuating meteorological conditions, the Anaerobic Digestion Model no. 1 (ADM1) is mechanistically extended in this paper to incorporate temporal changes into temperature-dependent parameters by defining inhibition functions for microbial activities using the cardinal temperature model, and accounting for the lag in microbial adaptation to temperature fluctuations using a time-lag adaptation function. Thereafter, given that temperature fluctuations are a significant disturbance, a control framework based on Model Predictive Control (MPC) is developed to regulate the feeding flow rate and to ensure stable production rates despite temperature disturbances without relying on direct temperature control. An adaptive MPC approach is formulated based on a linear input–output model, where the parameters of the linear model are updated online to capture the nonlinear dynamics of the process and frequent changes in the dynamics accurately. In addition, a fuzzy logic system is employed to assign a reference trajectory for the production rate based on the temperature and its rate of change. Integrating this fuzzy logic system with the MPC controller enhances the production rate on warm days and avoids the operational failure in production on cold days. Additionally, to enhance biogas production rates, the feasibility of utilizing a portion of the produced biogas for external heating purposes is also investigated. It is demonstrated that by utilizing the proposed MPC approach, the additional amount of feed for the digester to produce methane required for a self-consumption biogas-fueled heating system can be calculated according to the meteorological variations. This enhances the process performance and stability. Finally, a thermally optimized dome digester semi-buried in the ground, operating under climate conditions of The Netherlands is considered as a case study to validate the extended model in agreement with biological and physicochemical behaviors of real-world applications, and to demonstrate the effectiveness of the proposed control system in handling temperature changes and enhancing performance.

Oil palm empty fruit bunch (OPEFB) is an abundant organic waste in Malaysia that is often disposed of through field burning. A previous study has shown that solar-driven steam gasification of OPEFB can produce hydrogen-rich syngas with an energy upgrade factor of 1.2 and a carbon conversion efficiency of 95.1 %. Beyond its potential as a biofuel, OPEFB can also act as a carbon sink, capturing photosynthetically stored carbon. This study explores the potential of amplifying OPEFB's negative carbon emissions through solar-driven gasification, using CO₂ as the gasifying agent. In this work, a Central Composite Design (CCD) approach was employed to assess the influence of temperature (1100–1300 °C) and CO₂/OPEFB molar ratio (1.6–3.0) on H₂/CO molar ratio and energy upgrade factor, with a constant OPEFB flow rate of 1.8 g/min. The results demonstrated that at an energy upgrade factor of 1.4, 94.9 % of the total carbon was converted into syngas with a H₂/CO molar ratio of 0.3. The maximum observed net carbon capture yield of 0.4 g C/g OPEFB was achieved at 1300 °C and a CO₂/OPEFB molar ratio of 3.0. The remaining carbon (94.4–95.7 wt %) was converted into biochar with low heavy metal content, which has potential as a soil enhancer.

Proteins and carbohydrates are both major biodegradable fractions in wastewater. Complexation with coexisting compounds, such as iron (Fe) and humic acids (HA), which are both commonly present in wastewater, could influence the different degradation rates of proteins and carbohydrates. Depending on the redox conditions, Fe exists as Fe(II) or Fe(III), with differing binding affinities and chemical behaviour. This research aims to systematically assess the complex interaction between Fe, protein, and HA compounds under aerobic and anaerobic conditions. The results showed that the addition of Fe(III) and HA to a protein solution inhibited its hydrolysis rate by more than 90 % under aerobic conditions. In contrast, interactions between the same compounds and carbohydrates were much weaker and had a minimal effect on hydrolysis rates. Complexation with Fe, proteins, and HA was indicated by increased molecular sizes and reduced concentrations of free iron, protein, and HA. FTIR results showed that Fe(III) formed complexes with proteins and HA through electrostatic and coordination bonds involving various functional groups. Anaerobic reduction of Fe(III) to Fe(II) by hydrazine resulted in weaker binding and the formation of smaller, less stable protein–humic acid complexes. These findings suggested that modulating Fe complexation under alternating aerobic and anaerobic conditions, such as those found in redox-cycling wastewater treatment, can be a promising strategy to enhance protein degradation.

Particle-bubble collisions in dissolved air flotation (DAF) systems play a crucial role in the removal of total suspended solids (TSS). DAF particle-bubble collision models incorporate factors such as particle diameters, charge and density, bubble diameters, and collision factors. The challenge lies in accounting for the wide range of particle and bubble sizes and obtaining complex model inputs. To address this, a simplified model for TSS removal in DAF units was established using low-cost laboratory measurements, including particle size distribution and density. Additionally, microbubble diameter profiles were derived from bubble velocities using particle image velocimetry software (PIV). Six independent variables, encompassing influent particle characteristics (such as particle size distribution and density) and DAF running characteristics (temperature, contact zone detention time, inflow and recycle flows), were employed in the simplified model. The model's accuracy was evaluated using a laboratory-scale DAF system with two different influents: Delft canal water and anaerobic sludge. The predicted TSS removal from the simplified model aligned well with the laboratory-scale DAF results, yielding removal efficiencies of 68 ± 1 % and 77 ± 3 % for Delft canal water and anaerobic sludge, respectively. Furthermore, when the simplified model was applied to two full-scale DAF systems, it successfully identified an underperforming system (DAF2) with a TSS removal efficiency of 91 %, contrasting with the theoretical removal model-predicted efficiency of 98 %. This study highlights the utility of combining bubble size distribution measured by PIVlab and particle size distribution obtained using FIJI-ImageJ as an economical and efficient approach to acquiring the necessary inputs for predicting TSS removal in DAF systems.

Wastewater resources can be used to produce microbial protein for animal feed or organic fertiliser, conserving food chain resources. This investigation has employed the fermented sewage to photoheterotrophically grown purple non-sulfur bacteria (PNSB) in a 2.5 m³ pilot-scale raceway-pond with infrared light to produce proteinaceous biomass. Fermented sewage with synthetic media consisting of sodium acetate and propionic acids at a surface-to-volume (S/V) ratio of 10 m²/m³ removed 89%, 93%, and 81% of chemical oxygen demand, ammonium nitrogen, and orthophosphate, respectively; whereas respective removal in fermented sewage alone without synthetic media was 73%, 73%, and 72% during batch operation of 120 h. The biomass yield of 0.88–0.95 g COD_biomass /g COD_removed with protein content of 40.3 ± 0.3%–43.9 ± 0.2% w/w was obtained for fermented sewage with synthetic media. The results revealed enhanced possibility of scaling-up the raceway reactor to recover resources from municipal wastewater and enable simultaneous high-rate PNSB single-cell protein production.

Purple Phototrophic Bacteria (PPB) are increasingly being applied in resource recovery from wastewater. Open raceway-pond reactors offer a more cost-effective option, but subject to biological and environmental perturbations. This study proposes a hierarchical control system based on Adaptive Generalized Model Predictive Control (AGMPC) for PPB raceway reactors. The AGMPC uses simple linear models updated adaptively to project the complex process dynamics and capture changes. The hierarchical approach uses the AGMPC controller to optimize PPB growth as the core of the system. The developed supervisory layer adjusts set-points for the core controller based on two operational scenarios: maximizing PPB concentration for quality, or increasing yield for quantity through effluent recycling. Lastly, due to competing PPB and non-PPB bacteria during start-up phase, an override strategy for this transition is investigated through simulation studies. The Purple Bacteria Model (PBM) simulates this process, and simulation results demonstrate the control system's effectiveness and robustness.

High Pressure Hydrogenotrophic Denitrification (HPHD) provided a promising alternative for efficient and clean nitrate removal. In particular, the denitrification rates at low temperature could be compensated by elevated H₂ partial pressure. However, nitrite reduction was strongly inhibited while nitrate reduction was barely affected at low temperature. In this study, the nitrate reduction gradually recovered under long-term low temperature stress, while nitrite accumulation increased from 0.1 to 41.0 mg N/L. The activities of the electron transport system (ETS), nitrate reductase (NAR), and nitrite reductase (NIR) decreased by 45.8 %, 27.3 %, and 39.3 %, respectively, as the temperature dropped from 30 °C to 15 °C. Real time quantitative PCR analysis revealed that the denitrifying gene expression rather than gene abundance regulated nitrogen biotransformation. The substantial nitrite accumulation was attributed to the significant up-regulation by 54.7 % of narG gene expression and down-regulation by 73.7 % of nirS gene expression in hydrogenotrophic denitrifiers. In addition, the nirS-gene-bearing denitrifiers were more sensitive to low temperature compared to those bearing nirK gene. The dominant populations shifted from the genera Paracoccus to Hydrogenophaga under long-term low temperature stress. Overall, this study revealed the microbial mechanism of high nitrite accumulation in hydrogenotrophic denitrification at low temperature.

This paper focuses on the development of linear Switched Box–Jenkins (SBJ) models for approximating complex dynamical models of biological wastewater treatment processes. We discuss the adaptation of these processes to the SBJ framework, showing the model's generality and flexibility as a class of switched systems that can offer accurate predictions for complex and nonlinear dynamics. This approach of modeling enables real-time data reconciliation from experiments and allows the design of model-based control strategies. Through the extension of the Outer Bounding Ellipsoids (OBEs) algorithm, the paper introduces an online two-stage parameter identification algorithm that effectively handles bounded disturbances for SBJ models. Using the OBE method relaxes the stochastic assumptions on disturbances, which may not be satisfied in practice, particularly for biological and environmental fluctuations. The proposed decomposed OBE algorithm separately identifies the switching patterns and parameters of linear submodels, conducting parameter identification in two distinct phases for input/output and disturbance/output submodels. The efficacy of this approach is shown via simulation results validated against both ADM1 and PBM models, demonstrating the proposed algorithm's capability to accurately predict outputs from different biological wastewater treatment models.

Background: Elevated CO₂ partial pressure (pCO₂) has been proposed as a potential steering parameter for selective carboxylate production in mixed culture fermentation. It is anticipated that intermediate product spectrum and production rates, as well as changes in the microbial community, are (in)directly influenced by elevated pCO₂. However, it remains unclear how pCO₂ interacts with other operational conditions, namely substrate specificity, substrate-to-biomass (S/X) ratio and the presence of an additional electron donor, and what effect pCO₂ has on the exact composition of fermentation products. Here, we investigated possible steering effects of elevated pCO₂ combined with (1) mixed substrate (glycerol/glucose) provision; (2) subsequent increments in substrate concentration to increase the S/X ratio; and (3) formate as an additional electron donor. Results: Metabolite predominance, e.g., propionate vs. butyrate/acetate, and cell density, depended on interaction effects between pCO₂–S/X ratio and pCO₂–formate. Individual substrate consumption rates were negatively impacted by the interaction effect between pCO₂–S/X ratio and were not re-established after lowering the S/X ratio and adding formate. The product spectrum was influenced by the microbial community composition, which in turn, was modified by substrate type and the interaction effect between pCO₂–formate. High propionate and butyrate levels strongly correlated with Negativicutes and Clostridia predominance, respectively. After subsequent pressurized fermentation phases, the interaction effect between pCO₂–formate enabled a shift from propionate towards succinate production when mixed substrate was provided. Conclusions: Overall, interaction effects between elevated pCO₂, substrate specificity, high S/X ratio and availability of reducing equivalents from formate, rather than an isolated pCO₂ effect, modified the proportionality of propionate, butyrate and acetate in pressurized mixed substrate fermentations at the expense of reduced consumption rates and increased lag-phases. The interaction effect between elevated pCO₂ and formate was beneficial for succinate production and biomass growth with a glycerol/glucose mixture as the substrate. The positive effect may be attributed to the availability of extra reducing equivalents, likely enhanced carbon fixating activity and hindered propionate conversion due to increased concentration of undissociated carboxylic acids.

Dynamical systems and processes that either exhibit non-smooth behaviours (e.g. through logic control or natural phenomena) or work in different modes of operation are usually represented using hybrid systems models, i.e. mathematical models that combine continuous dynamics with discrete-event dynamics. Identification of a hybrid system includes finding switching patterns and identification of model parameters to obtain a data-driven model. This survey paper provides a systematic review of models (how to parameterize the system) and methods (how to identify unknown parameters) proposed for hybrid system identification with an exposition of recent advances and developments, and further research directions.

Film forming amines (FFA) are corrosion inhibitors added to power plant water. The major concern associated with their application is the thermal stability in the high temperature power plant water medium, along with the risk of decomposition into low molecular weight organic acids that can cause corrosive damages in the water/steam cycle. However, there is still a lack of sufficient data on the thermal stability of FFA corrosion inhibitors. This paper presents a comprehensive critical review and state-of-the-art assessment of the results obtained from studying the thermolysis of FFA corrosion inhibitors in power plant water/steam cycle conditions, highlighting the relevance for practical application and research needs. Temperature, exposure time, initial concentration, and alkalizing agents were identified as key factors influencing the thermal stability of FFA in high temperature power plant water. Organic acids are found in concentrations harmless to metal tubes. Advanced scientific background information and additional research are required on this topic.

Purple phototrophic bacteria (PPB) show an underexplored potential for resource recovery from wastewater. Raceway reactors offer a more affordable full-scale solution on wastewater and enable useful additional aerobic processes. Current mathematical models of PPB systems provide useful mechanistic insights, but do not represent the full metabolic versatility of PPB and thus require further advancement to simulate the process for technology development and control. In this study, a new modelling approach for PPB that integrates the photoheterotrophic, and both anaerobic and aerobic chemoheterotrophic metabolic pathways through an empirical parallel metabolic growth constant was proposed. It aimed the modelling of microbial selection dynamics in competition with aerobic and anaerobic microbial community under different operational scenarios. A sensitivity analysis was carried out to identify the most influential parameters within the model and calibrate them based on experimental data. Process perturbation scenarios were simulated, which showed a good performance of the model.