Electrochemical investigation of novel reference electrode Ni/Ni(OH)₂ in comparison with silver and platinum inert quasi-reference electrodes for electrolysis in eutectic molten hydroxide

An efficient and green energy carrier hydrogen (H2) generation via water splitting reaction has become a major area of focus to meet the demand of clean and sustainable energy sources. In this research, the splitting steam via eutectic molten hydroxide (NaOH–KOH; 49–51 mol%) electrolysis for hydrogen gas production has been electrochemically investigated at 250–300 °C. Three types of reference electrodes such as a high-temperature mullite membrane Ni/Ni(OH)2, quasi-silver and quasi-platinum types were used. The primary purpose of this electrode investigation was to find a suitable, stable, reproducible and reusable reference electrode in a molten hydroxide electrolyte. Cyclic voltammetry was performed to examine the effect on reaction kinetics and stability to control the working electrode at different scan rate and molten salt temperature. The effect of introducing water to the eutectic molten hydroxide via the Ar gas stream was also investigated. When the potential scan rate was changed from 50 to 150 mV s−1, the reduction current for the platinum wire working electrode was not changed with newly prepared nickel reference electrode that designates its stability and reproducibility. Furthermore, increasing the operating temperature of molten hydroxides from 250 to 300 °C the reduction potential of the prepared nickel reference electrode is slightly positive shifted about 0.02 V. This suggests that it has good stability with temperature variations. The prepared nickel and Pt reference electrode exhibited stable and reliable cyclic voltammetry results with and without the presence of steam in the eutectic molten hydroxide while Ag reference electrode exposed positive shifts of up to 0.1 V in the reduction potential. The designed reference electrode had a more stable and effective performance towards controlling the platinum working electrode as compared to the other quasi-reference electrodes. Consequently, splitting steam via molten hydroxides for hydrogen has shown a promising alternative to current technology for hydrogen production that can be used for thermal and electricity generation

Assessment of biomass energy potential for SRC willow woodchips in a pilot scale bubbling fluidized bed gasifier

The current study investigates the short rotation coppice (SRC) gasification in a bubbling fluidized bed gasifier (BFBG) with air as gasifying medium. The thermochemical processes during combustion were studied to get better control over the air gasification and to improve its effectiveness. The combustion process of SRC was studied by different thermo-analytical techniques. The thermogravimetric analysis (TGA), derivative thermogravimetry (DTG), and differential scanning calorimetry (DSC) were performed to examine the thermal degradation and heat flow rates. The product gas composition (CO, CO2, CH4 and H2) produced during gasification was analyzed systematically by using an online gas analyzer and an offline GC analyzer. The influence of different equivalence ratios on product gas composition and temperature profile was investigated during SRC gasification. TG/DTG results showed degradation occur in four stages; drying, devolatilization, char combustion and ash formation. Maximum mass loss ~70% was observed in devolatilization stage and two sharp peaks at 315–500 °C in TG/DSC curves indicate the exothermic reactions. The temperature of gasifier was increased in the range of 650–850 °C along with the height of the reactor with increasing equivalent ratio (ER) from 0.25 to 0.32. The experimental results showed that with an increment in ER from 0.25 to 0.32, the average gas composition of H2, CO, CH4 decreased in the range of 9–6%, 16–12%, 4–3% and CO2 concentration increased from 17 to 19% respectively. The gasifier performance parameters showed a maximum high heating value (HHV) of 4.70 MJ/m3, Low heating value (LHV) of 4.37 MJ/m3 and cold gas efficiency (CGE) of 49.63% at 0.25 ER. The ER displayed direct effect on carbon conversion efficiency (CCE) of 95.76% at 0.32 ER and tar yield reduced from 16.78 to 7.24 g/m3 with increasing ER from 0.25 to 0.32. All parametric results confirmed the reliability of the gasification process and showed a positive impact of ER on CCE and tar yield

Torrefied biomass fuels as a renewable alternative to coal in co-firing for power generation

This study aims to assess the torrefaction of biomass as alternative renewable energy fuel to coal during co-firing. It was evaluated that torrefaction improves biomass grindability to such an extent that it can be used in coal mills with coal in co-firing without capital intensive modification. Torrefaction of beech wood was performed on a batch scale reactor at three different temperatures (200, 250 and 300 °C) with 30 min of residence time. The chemical structural changes in torrefied biomass were investigated with binding energies and FTIR (Fourier transform infrared) analysis. Monocombustion and co-combustion tests were performed to examine the combustion behaviour regarding flue gas emissions (CO, NOx and SO2) at 0.5, 1.5 and 2.5 m distance from the burner opening along with fly ash analysis. The FTIR and binding energies showed that lignin hardly affected during light torrefaction while hemicellulosic material was significantly depleted. The Hardgrove grindability index (HGI) was calculated with three methods (DIN51742, IFK and ISO). The medium temperature torrefied biomass (MTTB) yields HGI value in the range of 32–37 that was comparable with HGI of El Cerrejon coal (36–41). A slight change in temperature enabled the torrefied beech wood to be co-milled with coal without capital intensive modification and improved grindability. Comparing the combustion behaviour of single fuels, low temperature torrefied biomass (LTTB) produces less amount of NOx (426 mg/m3), CO (0.002 mg/m3) and SO2 (2 mg/m3) as compared MTTB and raw beech wood. In the case of co-combustion, it was found that blending of coal with raw biomass does not show a stable behaviour. However, premixing of 50% of coal with 50% of torrefied biomasses (MTTB and LTTB) gives most stable behaviour and reduces NOx almost 30% and SOx up to almost 50% compared to coal. The fly ash contents analysis proved that K2O contents much decreased during co-firing of coal and torrefied fuels that could cause ash related issues during combustion of raw biomass

Smart Predictor for Spontaneous Combustion in Cotton Storages Using Wireless Sensor Network and Machine Learning

The splendid technological inventions supersede many traditional agricultural monitoring systems. In the last decade, a variety of new techniques and tools are proposed to monitor storage areas, which provide more safe and secure storage for different crops. The term storage area monitoring is supposed to check and avoid fire hazards, whereas numerous other hazards also need attention. One such hazard to cotton storage is spontaneous combustion, a process by which an element having comparatively low ignition temperature (hay, straw, peat, etc.) starts to relieve heat. In the presence of spontaneous combustion and lack of oxygen, if cotton catches any sparks from bales or physicochemical heat to ignite, the combustion can convert in to smoldering, and it can last up to several days without being discovered. Consequently, the actual fire occurs, cotton silently smoldering which not only affects cotton quality but also became the reason of big fire event. Many researchers propose valuable tools and techniques based on laboratory methods and modern techniques as well for detection and prevention of security hazards in storages. However, there is no standalone efficient tool/technique to monitor the storage area for spontaneous combustion. In current research, we propose an efficient wireless sensor network (WSN) and machine learning- (ML-) based storage area monitoring system for early prediction of spontaneous combustion in the cotton storage area. The WSN is used to collect real-time values from storage field by different combinations of sensors and send this over the network, where data is processed to identify spontaneous combustion and distribute the prediction results to the end user. The real-time data collection and ML-based analysis make the system efficient and reliable. The efficiency of the current system is verified by presenting two groups of cotton stored with different conditions. The results showed that the proposed system is able to detect spontaneous combustion well in time with a 95% accuracy rate