Parametric study of heavy metal partitioning in a pilot scale incinerator burning simulated municipal solid waste

Incineration is a combustion process used to convert toxic waste to benign gases and residue in presence of excess air. The products of the incineration process are bottom ash, fly ash and the flue gas. Most of the metal-based phases formed in incineration are toxic and their emissions need to be strictly controlled. Therefore, behavior of metal species during incineration must be well understood. Such understanding is possible based on the experimental identification of the metal phases formed in the waste combustion and determination of their concentration in various incineration products. A pilot-scale incinerator of 140,000 Btu/hr capacity was constructed, characterized and operated at NJIT. A synthetic fuel representative of the municipal solid waste in the United States was formulated and produced in 600 lb batches. The synthetic fuel was in the form of solid pellets and was characterized by standard ASTM tests. The solid fuel contained Fe and SiO2, and was doped with trace amounts of Al, Ni, Cr, Hg and Pb. Several experiments were performed on the incinerator with varying fuel-air equivalence ratio and both gaseous and condensed products were sampled.Atomic absorption spectroscopy was used to identify metal concentrations in the ashes and the flue gas. X-ray diffraction was used to identify metal phases in the bottom ash and the fly ash. Scanning electron microscope was used to study the morphology of the ash particles and energy dispersive X-ray spectroscopy was used to identify the spatial composition of the ash particles. Size distributions of the fly ash particles were obtained using sieves and optical microscopy. It has been observed that the fly ash particles have bimodal size distribution and that the particles of different sizes have different elemental and phase compositions. Thermodynamic equilibrium computations for the incineration process were conducted to obtain the adiabatic flame temperature and identify the metal phases produced at equilibrium conditions

KINEMATIC SYNTHESIS OF CENTRAL-LEVER STEERING MECHANISM FOR FOUR WHEEL VEHICLES

A central lever steering mechanism has been synthesized to obtain five precision points for a four-wheel vehicle using Hooke and Jeeves optimization method. This compound mechanism has been studied as two identical crossed four-bar mechanisms arranged in series. The optimization has been carried out for one crossed four-bar mechanism only instead of the entire mechanism. The number of design parameters considered for the optimization is two. The inner wheel has been considered to rotate up to 52 degrees. The steering error, pressure angle and mechanical advantage of the proposed mechanism have been compared with those achieved by the Ackermann steering mechanism. The proposed mechanism has less steering error, more favourable pressure angle and increased mechanical advantage. The method of compounding the mechanism is also applicable when the central lever is offset from the longitudinal axis of the vehicle

Forecasting of Engine Performance for Gasoline-Ethanol Blends using Machine Learning

The incorporation of alternative fuels in the automotive domain has brought a new paradigm to tackle the environmental and energy crises. Therefore, it is of interest to test and forecast engine performance with blended fuels. This paper presents an experimental study on gasoline-ethanol blends to test and forecast engine behavior due to changes in the fuel. This study employed a machine learning (ML) technique called TOPSIS to forecast the performance of a slightly higher blend fuelled engine based on experimental data obtained from the same engine running on 0% ethanol blend (E0) and E10 fuels under full load conditions. The engine performance predictions of this ML model were validated for 15% ethanol blend (E15) and further used to predict the engine performance of 20% ethanol blend fuel. The prediction R2 score for the ML model was found to be greater than 0.95 and the MAPE range was 1% to 5% for all observed engine performance attributes. Thus, this paper presents the potential of TOPSIS methodology-based ML predictions on blended fuel engine performance to shorten the testing efforts of blended fuel engines. This methodology may help to faster incorporate higher blended fuels in the automotive sector

Forecasting of Engine Performance for Gasoline-Ethanol Blends using Machine Learning

The incorporation of alternative fuels in the automotive domain has brought a new paradigm to tackle the environmental and energy crises. Therefore, it is of interest to test and forecast engine performance with blended fuels. This paper presents an experimental study on gasoline-ethanol blends to test and forecast engine behavior due to changes in the fuel. This study employed a machine learning (ML) technique called TOPSIS to forecast the performance of a slightly higher blend fuelled engine based on experimental data obtained from the same engine running on 0% ethanol blend (E0) and E10 fuels under full load conditions. The engine performance predictions of this ML model were validated for 15% ethanol blend (E15) and further used to predict the engine performance of 20% ethanol blend fuel. The prediction R2 score for the ML model was found to be greater than 0.95 and the MAPE range was 1% to 5% for all observed engine performance attributes. Thus, this paper presents the potential of TOPSIS methodology-based ML predictions on blended fuel engine performance to shorten the testing efforts of blended fuel engines. This methodology may help to faster incorporate higher blended fuels in the automotive sector