Burcu Gunes

Higher heating values (HHV) is a very useful parameter for assessing the design and large-scale operation of biomass-driven energy systems. HHV is conventionally measured experimentally with an adiabatic oxygen bomb calorimeter. This procedure is often time-consuming and expensive. Furthermore, limited access to the required facilities is the main bottleneck for researchers. Empirical linear and nonlinear models have initially been proposed to address these concerns. However, most of the models showed discrepancies with experimental results. Data-driven machine learning (ML) methods have also been adopted for HHV predictions due to their suitability for nonlinear problems. However, most ML correlations are based on proximate or ultimate analysis. In addition, the models are only applicable to either the originator biomass or one specific type. To address these shortcomings, a total of 227 biomass datasets based on four classes of biomass, including agricultural residue, industrial waste, energy crop, and woody biomass, were employed to develop and verify three different ML models, namely artificial neural network (ANN), decision tree (DT) and random forest (RF). The model incorporates proximate and ultimate analysis data and biomass as input features. RF model is identified as the most reliable because of its lowest mean absolute error (MAE) of 1.01 and mean squared error (MSE) of 1.87. The study findings can be used to predict HHV accurately without performing experiments.

Gas-liquid mass transfer is a significant issue in most bioprocesses. More importantly, gas-liquid mass transfer limitation requires further attention during syngas fermentation (SNF). The gas-liquid mass transfer of gaseous substrates (CO, CO2, and H2) into the fermentation broth is a rate-limiting step in SNF that leads to low productivity and poor economic feasibility. Enhancing this process during SNF can result in high efficiency, better production of ethanol, as well as lower energy consumption. While pressure and power input are important factors for improving reactor design, adding magnetic nanoparticles (MNPs) in the liquid phase is critical to achieving an enhanced gas-liquid mass transfer. The present study reviewed recent advances in the application of MNPs for an improved gas-liquid mass transfer during syngas fermentation. A brief overview of SNF and the effects of MNPs on SNF process are outlined. In addition, the hydrodynamic effect at the gas-liquid boundary is also seen as a mechanism in which nanoparticles increase mass transfer, and the mechanism is elucidated in detail.

The remarkable adsorption capacity of graphene-derived materials has prompted their examination in composite materials suitable for deployment in treatment of contaminated waters. In this study, crosslinked calcium alginate–graphene oxide beads were prepared and activated by exposure to pH 4 by using 0.1M HCl. The activated beads were investigated as novel adsorbents for the removal of organic pollutants (methylene blue dye and the pharmaceuticals famotidine and diclofenac) with a range of physicochemical properties. The effects of initial pollutant concentration, temperature, pH, and adsorbent dose were investigated, and kinetic models were examined for fit to the data. The maximum adsorption capacities q_max obtained were 1334, 35.50 and 36.35 mg g⁻¹ for the uptake of methylene blue, famotidine and diclofenac, respectively. The equilibrium adsorption had an alignment with Langmuir isotherms, while the kinetics were most accurately modelled using pseudo- first-order and second order models according to the regression analysis. Thermodynamic parameters such as ΔG°, ΔH° and ΔS° were calculated and the adsorption process was determined to be exothermic and spontaneous.

The composition of challenging matrices must be fully understood in order to determine the impact of the matrix and to establish suitable treatment methods. Rendering condensate wastewater is a complex matrix which is understudied. It is produced when the vapour from rendering facilities (heat processing of slaughterhouse waste material) is cooled as a liquid for discharge. This study offers a full physicochemical characterisation of rendering condensate wastewater and its potential for valorisation via production of viable by-products. A study of seasonal variation of levels of dissolved oxygen, chemical oxygen demand, total nitrogen and ammonia was carried out on the wastewater. The results show that the wastewater was high strength all year-round, with a chemical oxygen demand of 10,813 ± 427 mg/L and high concentrations of total Kjeldahl nitrogen (1745 ± 90 mg/L), ammonia (887 ± 21 mg/L), crude protein (10,911 ± 563 mg/L), total phosphorous (51 ± 1 mg/L), fat and oil (11,363 ± 934 mg/L), total suspended solids (336 ± 73 mg/L) and total dissolved solids (4397 ± 405 mg/L). This characterisation demonstrates the requirement for adequate treatment of the condensate before releasing it to the environment. While there is a reasonably constant flow rate and dissolved oxygen level throughout the year, higher chemical oxygen demand, total nitrogen and ammonia levels were found in the warmer summer months. From this study, rendering condensate slaughterhouse wastewater is shown to have potential for production of marketable goods. These products may include ammonium sulphate fertilizer, protein supplements for animal feeds and recovery of acetic acid calcium hydroxyapatite, thus enhancing both the financial and environmental sustainability of slaughterhouse operations. This work demonstrates a valuable assessment of a complex wastewater, while taking advantage of on-site access to samples and process data to inform the potential for wastewater reuse.

Whiskey distillery waste streams consisting of pot ale (liquid residue) and spent grain (solid residue) are high strength organic wastes and suitable feedstock for anaerobic digestion (AD) from both economic and environmental stand points. Anaerobic digestion of pot ale and pot ale/spent grain mixtures (with mixing ratios of 1:1, 1:3, and 1:5 by wet weight) was performed after implementation of a novel hybrid pre-treatment (combined chemical and mechanical) in order to modify lignocellulosic structure and ultimately enhance digestion yield. Lignin, hemicellulose, and cellulose fractions were determined before and after chemical pre-treatment. Effects of different inoculum rates (10–30–50% on wet basis) and beating times (0–7.5–15 min) on anaerobic digestion of pot ale alone and of pot ale/spent grain mixtures were investigated in lab scale batch mode with a major focus of optimising biogas yield by using response surface methodology (RSM) in Design Expert Software. The highest biogas yields of 629 ± 8.5 mL/g vs. (51.3% CH₄) and 360 ± 10 mL/g vs. (55.0 ± 0.4) with anaerobic digestion of pot ale alone and spent grain mix after 1M NaOH and 7.5 min beating pre-treatments with 50% inoculum ratio respectively. The optimum digestion conditions to maximise the biogas quality and quantity were predicted as 10 and 13 min beating times and 32 and 38 °C digestion temperatures for anaerobic digestion of pot ale alone and spent grain mix respectively.