The air gasification of Palm Kernel Shells (PKS) using coal bottom ash (CBA) as a catalyst has been performed in a fixed-bed gasifier. The impact of three process parameters, namely, temperature (575-775 °C), air flowrate (1.5-45 litter/min) and catalyst loading (0-30 wt.%) has been investigated on the product gas yield. The composition of the H₂ product is computed to be a maximum of 28 vol.% at 875 °C. The air flowrate has a direct relation with H₂ production. The catalysts used have demonstrated a positive impact on the carbon conversion efficiency, showing the increase in carbon-containing gases in the product gas due to the increases in gas yield. A Non-linear Autoregressive Network with exogenous inputs (NARX) neural network has been used to predict the gaseous flowrate dynamically in order to improve gasification performance. The predicted results from the NARX network demonstrate good agreement with the experimental study with R² ≥ 0.99.

Experimental setup for catalytics air gasification of PKS

The experimental arrangement used for the air catalytic gasification of PKS consisted of an air supply unit (air compressor & rotameter), electric heater, thermocouple, and temperature control unit, gasifier unit, gas cleaning and cooling unit, online gas analyser system, and computer as illustrated in Fig 1. The height and external diameter of the fixed bed gasifier was 750 mm 67.0 mm, respectively. The external jacketed heaters were installed to heat the gasifier to the desired temperature. The temperature of the gasifier combustion zone was controlled and measured by a PID microcontroller and a thermocouple installed at the top of the gasifier body. The gasifier was pre-heated up to the target temperature, after that, a pre-mixed mixture of PKS and CBA was supplied to the gasifier from its feeding point located at the top of the gasifier. Air was supplied to the gasifier in the range of 1.5-3.5 liters/min. The gases produced during gasification were passed through the filter in order to remove the liquid and solid impurities. The gases were cold and cleaned by passing through the cooling and cleaning units. The composition of produced gases was analysed by using an online gas analyser system with a data sampling interval of one second. Once the feedstock was consumed, the electric heater was switched off, and the air supply was stopped, and the gasifier was left to cool down. Finally, any unburnt residual (ash and char) was collected from the bottom of the gasifier and measured prior to further use.

NARX network topology with input and output delays

Improved Gasification Efficiency: The use of a catalyst like waste bottom ash can enhance the gasification process by promoting reactions at lower temperatures, increasing the conversion of biomass into synthesis gas (syngas) containing hydrogen and carbon monoxide. This leads to improved overall gasification efficiency.

Reduced Tar Formation: Catalytic gasification tends to suppress the formation of tars, which are undesirable byproducts of biomass gasification. This results in cleaner syngas production with lower tar content, making downstream processes such as gas cleaning and utilization more efficient and cost-effective.

Utilization of Waste Materials: By using waste bottom ash as a catalyst, the process contributes to waste management by repurposing a material that would otherwise need disposal. This aligns with sustainability goals and reduces environmental impact.

NARX Neural Network Modeling: Integration of NARX neural network modeling allows for accurate prediction and control of the gasification process. By using historical data and real-time inputs, the neural network can optimize operating conditions, maximize gasification efficiency, and minimize energy consumption.

Selected NARX neural network structure for the prediction of gas composition

Technology Assessment: Understand the technical feasibility, advantages, and limitations of ACBG using PKS and waste bottom ash as a catalyst. Consider factors like efficiency, scalability, environmental impact, and cost-effectiveness compared to alternative technologies.

Market Analysis: Conduct market research to identify potential applications and industries where ACBG could be viable. This might include sectors such as renewable energy production, waste management, biomass utilization, and sustainable development initiatives.

Competitive Landscape: Assess existing competitors and similar technologies in the market. Understand their market share, pricing strategies, distribution channels, and technological advancements to gauge the competitive advantage of ACBG.

Demand Estimation: Estimate the potential demand for ACBG systems based on the identified market segments, considering factors such as energy demand, government policies and incentives, environmental regulations, and consumer preferences for sustainable energy solutions.

MSE performance of the NARX neural network