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Ethanol is known as the most widely used alternative fuel for spark-ignition engines. Compared to it, butanol has proved to be a very promising renewable fuel in recent years for desirable properties. The conjoint analysis on combustion, performance, and emissions characteristics of a port fuel injection spark-ignition engine fueled with butanol–gasoline blends was carried out. In comparison with butanol–gasoline blends with various butanol ratio (0–60 vol% referred as G100~B60) and conventional alcohol alternative fuels (methanol, ethanol, and butanol)–gasoline blends, it shows that B30 performs well in engine performance and emissions, including brake thermal efficiency, brake-specific fuel consumption, carbon monoxide, unburned hydrocarbons, and nitrogen oxides. Then, B30 was compared with G100 under various equivalence ratios ( Φ = 0.83–1.25) and engine loads (3 and 5-bar brake mean effective pressure). In summary, B30 presents an advanced combustion phasing, which leads to a 0.3%–2.8% lower brake thermal efficiency than G100 as the engine was running at the spark timing of gasoline’s maximum brake torque (MBT). Therefore, the sparking timing should be postponed when fueled with butanol–gasoline blends. For emissions, the lower carbon monoxide (2.3%–8.7%), unburned hydrocarbons (12.4%–27.5%), and nitrogen oxides (2.8%–19.6%) were shown for B30 compared with G100. Therefore, butanol could be a good alternative fuel to gasoline for its potential to improve combustion efficiency and reduce pollutant emissions.

In order to effectively predict the sieving efficiency of a vibrating screen, experiments to investigate the sieving efficiency were carried out. Relation between sieving efficiency and other working parameters in a vibrating screen such as mesh aperture size, screen length, inclination angle, vibration amplitude, and vibration frequency was analyzed. Based on the experiments, least square support vector machine (LS-SVM) was established to predict the sieving efficiency, and adaptive genetic algorithm and cross-validation algorithm were used to optimize the parameters in LS-SVM. By the examination of testing points, the prediction performance of least square support vector machine is better than that of the existing formula and neural network, and its average relative error is only 4.2%.