An overview of torrefied bioresource briquettes: quality-influencing parameters, enhancement through torrefaction and applications

Abstract In recent years, the need for clean, viable and sustainable source of alternative fuel is on the rampage in the global space due to the challenges posed by human factors including fossil induced emissions, fuel shortage and its ever-rising prices. These challenges are the major reason to utilize alternative source of energy such as lignocellulosic biomass as domestic and industrial feedstock. However, biomass in their raw form is problematic for application, hence, a dire need for torrefaction pre-treatment is required. The torrefaction option could ameliorate biomass limitations such as low heating value, high volatile matter, low bulk density, hygroscopic and combustion behaviour, low energy density and its fibrous nature. The torrefied product in powder form could cause air pollution and make utilization, handling, transportation, and storage challenging, hence, densification into product of higher density briquettes. This paper therefore provides an overview on the performance of torrefied briquettes from agricultural wastes. The review discusses biomass and their constituents, torrefaction pre-treatment, briquetting of torrefied biomass, the parameters influencing the quality, behaviour and applications of torrefied briquettes, and way forward in the briquetting sector in the developing world

Toward N-nitrosamines free water: Formation, prevention, and removal

This study elucidates the recent trends in the formation, prevention, and removal of N-nitrosamines such as Nnitrosodimethylamine(NDMA) from wastewater or drinking water. Reports are rife on the occurrence of NDMA in areas such as amine degradation during postcombustion CO2 capture (PCC), chlorinated/chloraminated and ozonated drinking water, smoked or cooked foods personal care, tobacco and pharmaceutical products. The major routes responsible for the formation of NDMA in portable waters include chlorination/ chloramination and ozonation. The major NDMA precursors are secondary, tertiary, and quaternary amines such as dimethylamine, diethanolamine, and triethanolamine. Due to the environmental and public health concerns posed by this contaminant, a proactive approach is necessary towards suppressing their occurrence, as well as their removal. Consequently, this study critically reviewed the formation, prevention, and removal of N-nitrosamines. The study discussed NDMA prevention techniques, such as physical adsorption, preoxidation, and biological activated carbon. The removal techniques discussed here include physicochemical (such as combined adsorption and microwave irradiation and UV photolysis), bioremediation, catalytic reduction, and dope technology. Irrespective of the effectiveness and seemingly economic viability of some of these technologies, preventing the occurrence of NDMA right from the outset is more potent because the treatments consume more energy

The Effects of Some Processing Parameters on Physical and Combustion Characteristics of Corncob Briquettes” An Unpublished

Abstract Corncobs are generated from maize (Zea mays). The residue is usually dumped and flared on the farms, where it constitutes fire, environmental and health hazards. Corncobs are potential feedstock for energy generation. This work investigated densification characteristics of corncobs. Corncobs were collected from farm dump at a moisture content of 10.96 dry bases, reduced and sieved into three particle sizes S 1 , S 2 ; and S 3 . Starch mutillage of 20, 25, and 30 % by weight of the residue was added as binder. The briquettes were produced using briquetting machine at pressures of 2.1, 4.2 and 6.6 MPa. The ASAE standard methods were used to determine the moisture contents and densities of the milled residues and briquettes. The compaction, density and relaxation ratios as well as percentage expansion of the briquettes were determined using ASAE standard methods. The mean moisture content of the corncob residue was 9.64 %, while that of relaxed briquettes was 7.46%. The value of bulk densities of the residue materials was 50.32 kg/m3. The initial, maximum and relaxed densities ranged from 151-235 kg/m3; 533-981kg/m3 and 307-417kg/m3 respectively. The compaction ratio ranged from 2.27 to 6.50. The maximum percentage volume reduction was 626%, while the axial and lateral relaxations were in the range of 0.62-9.85% and 0.64-3.63 respectively. The briquettes were stable up to six months. For the three processing parameters examined, binder ratio B1, particle size S3 and pressure P3 exhibited most positive attributes

Biodegradability and kinetic studies on biomethane production from okra (Abelmoschus esculentus) waste

Emerging from the energy crisis of 2008 in South Africa, climate change concerns and the global desire to reduce high ozone-depleting emissions, renewable energy sources like biogas are gaining wide acceptance in most localities for heating and electricity. The paucity of feedstock varieties is a major challenge plaguing the sustainability of this sector. Biomethane potential, biodegradability and degradation kinetics of organic substrates are essential for assessing the suitability of feedstocks for methane generation and the overall performance of the anaerobic digestion process in biogas plants. Waste from the vegetable okra (Abelmoschus esculentus) is a novel substrate; its biodegradability and degradation dynamics in biomethane production are largely unstudied, and were therefore the aims of this research. The substrate was digested for 25 days at the mesophilic condition and the biomethane potential data were recorded. Measured data of methane yield and the elemental composition of the substrate were used to fit five models (modified Gompertz, Stannard, transference function, logistic and first-order models) to predict degradation parameters and determine biodegradability of the substrate, respectively. Low lag phase (0.143 d), positive kinetic constant (0.2994/d) and the model fitness indicator (<10) showed that transference and first-order kinetic models predicted the methane yield better than did other growth functions. The experimental methane yield was 270.98 mL/gVS, theoretical methane yields were 444.48 mL/gVS and 342.06 mL/gVS and model simulation ranged from 267.5 mL/gVS to 270.89 mL/gVS. With a prediction difference of 0.03–1.28%, all growth functions acceptably predicted the kinetics of A. esculentus waste. The findings of this study offer information on this novel substrate important for its use in large-scale biogas production. Significance: Growing interest in biogas technology as an alternative energy source for both South African rural dwellers and industries, has mounted enormous pressure on known feedstocks, and instigated the search for novel substrates. Our study shows that okra waste is a viable feedstock for biogas production. The suitability of the first-order kinetic model over other models in predicting okra waste degradation was highlighted

Chemical Oxygen Demand removal rate comparative analysis using three different biogas digesters

The Chemical Oxygen Demand (COD) end value of effluent discharged from a bioreactor into the environment is a critical indicator of its capacity to pollute the environment. A comparative parametric analysis of COD removal rate using three different biogas digesters is presented.  The three bioreactors are Upflow Anaerobic Sludge Blanket (UASB), Upflow Bioreactor with Central Substrate Dispenser (UBCSD), and Continuous Stirred-Tank Reactor (CSTR). In order to select the most fitting bioreactor type among the three considered, experimentation was carried out using organic municipal waste (OMW) as substrate. A 10-day hydraulic retention time (HRT) was used, while cattle rumen microbes were used to improve digestion rate. UBCSD showed the highest level of percentage COD removal of 95.2%, followed by the CSTR with a value of 80.8%; while the UASB offered the lowest level of percentage COD removal of 79.0%.  This outcome indicates that effluent from the UBCSD digested substrate is more suitable and safer for use as organic fertilizer in agricultural practices. It similarly implies that a bioreactor with enhanced mixing capacity is safer for digestion

KINETIC STUDIES ON METHANE PRODUCTION FROM OKRA WASTES USING GROWTH FUNCTIONS

Degradation kinetics of organics is essential for assessing the performance of anaerobic digestion process. This study investigates the biomethane potentials (BMP) and kinetics of okra waste using AMPTS II BMP assay. Measured data of methane yield were used to fit five models (Modified Gompertz, Stannard, Transference function, Logistic and First-Order models), predict and determine organic degradation parameters of the substrate. Low lag phase (0.143d), positive kinetic constant (0.2994/d) and model fitness indicator (<10) showed that Transference and First-Order kinetic models predicted the methane yield better than other growth functions. The experimental methane yield was 270.98mL/gVS and model simulation (267.5- 270.89 mL/gVS). With %prediction difference (0.03-1.28%), all the growth functions acceptably predicted the kinetics of okra wasteMechanical and Industrial Engineerin

Experimental based comparative exergy analysis of a spark‐ignition Honda GX270 Genset engine fueled with LPG and syngas

Abstract The present study investigates three different fuels such as gasoline, liquefied petroleum gas (LPG), and syngas in spark‐ignition Honda GX270 Genset engine under wide‐open throttle position on its performance, combustion characteristic as well as availability analysis. The results showed that when the engine operated with gasoline fuel, the brake thermal efficiency was higher than that of LPG and syngas by 6.2% and 7.4%, respectively, throughout the engine load condition. Brake‐specific fuel consumption of the engine with syngas (660 g/kW h) and LPG fuel (812 g/kW h) was higher than that of the gasoline fuel (510 g/kW h) at the 4.5 kW of engine load. The engine emission results showed syngas operation caused a significant reduction in NOx by 58.4%, CO by 16.5%, HC by 23.2% compared to gasoline fuel at peak load conditions. On the other hand, exergy analysis concludes the exergy efficiency for all the test fuels increases with an increase in engine load due to a high rise in shaft output. At a 4.5 kW power output, the exergy efficiency of the engine was improved to 46.45% from 45.62% and 29.73% with syngas, gasoline, and LPG, respectively. The maximum exhaust gas availability has been observed as 24.51% of availability input for syngas at 100% load condition

Artificial neural networks vs. gene expression programming for predicting emission &amp; engine efficiency of SI operated on blends of gasoline-methanol-hydrogen fuel

While retaining environmental friendliness, robust modelling and enhancing spark ignition engine efficacy can be done using improved innovative fuel and unconventional robust hybrid tools. This study is the first to employ Al techniques such as artificial neural networks (ANN) and gene expression programming (GEP) to predict the performance and emissions of a gasohol/hydrogen-powered SI engine. The ANN was adopted to correlate the engine variables viz. engine speed and gasohol/hydrogen mix vs. responses namely brake thermal efficiency (BTE), brake specific energy consumption (BSEC), carbon monoxide (CO), hydrocarbon (HC), oxides of nitrogen (NOx) and carbon dioxide (CO2). GEP model was further employed to predict BTE, BSEC, CO, HC, NOx and CO2. To examine the prediction efficacy of both AI techniques, a set of advanced statistical approaches was used. A set of advanced statistical techniques were employed to test the prediction efficiency of both AI techniques. It was revealed that ANN outperformed the GEP since the values for R in the case of ANN were 0.9864–0.9998 whereas the values for R in the case of GEP were 0.9864–0.9994. Similarly, in the instance of R2, ANN outperformed GEP. Furthermore, Kling-Gupta efficiency was greater in the case of ANN (0.9684–0.9999) than in GEP (0.8912–0.9991). Both AI approaches, however, displayed great prognostic effectiveness in forecasting engine performance and emissions