This paper first describes a slow catalytic pyrolysis process used for synthesizing biodiesel from waste cooking oil (WCO) as a feedstock. The influence of variations in the catalyst type (sodium hydroxide and potassium hydroxide), and catalyst concentration (0.5, 1.0, 3.0, 5.0, 7.0 and 10.0% by weight) on both the pyrolysis temperature range and biodiesel yield were investigated. The results suggested that sodium hydroxide (NaOH) was more effective than potassium hydroxide (KOH) as catalysts and that the highest yield (around 70 wt.%) was observed for a NaOH concentration of about 1 wt.% The resultant pyrolysis temperature range was also significantly lower for NaOH catalyst, thus suggesting overall lower energy consumption. Compared to conventional diesel, the synthesized biodiesel exhibited relatively similar physical properties and calorific value. The biodiesel was subsequently blended with diesel fuel in different blend ratios of 0, 20, 40, 60, 80 and 100% by volume of biodiesel and were later tested in a compression ignition engine. Brake thermal efficiency and specific fuel consumption were observed to be worse with biodiesel fuel blends particularly at higher engine load above 50%. However, NOx emission generally decreased with increasing blend ratio across all engine load, with greater reduction observed at higher engine load. Similar observation can also be concluded for CO emission. In contrast, lower hydrocarbon (HC) emission from the biodiesel fuel blends was only observed for blend ratios no higher than 40%. Particulate emission from the biodiesel fuel blends did not pose an issue given its comparable smoke opacity to diesel observed during the engine test. The in-cylinder peak pressures, temperature and heat release rate of biodiesel fuel blends were lower than diesel. Overall, biodiesel fuel blends exhibited shorter ignition delays when compared to diesel fuel.

Improved thermal efficiency in energy-intensive metal-reheating furnaces has attracted much attention recently in efforts to reduce both fuel consumption, and CO₂ emissions. Thermal efficiency of these furnaces has improved in recent years (through the installation of regenerative or recuperative burners), and improved refractory insulation. However, further improvements can still be achieved through setting up reference values for the optimal set-point temperatures of the furnaces. Having a reasonable expression of objective function is of particular importance in such optimisation. This paper presents a function value-based multi-objective optimisation where the objective functions, which address such concerns as discharge temperature, temperature uniformity, and specific fuel consumption, are dependent on each other. Hooke-Jeeves direct search algorithm (HJDSA) was used to minimise the objective functions under a series of production rates. The optimised set-point temperatures were further used to construct an artificial neural network (ANN) of set-point temperature in each control zone. The constructed artificial neural networks have the potential to be incorporated into a more advanced control solution to update the set-point temperatures when the reheating furnace encounters a production rate change. The results suggest that the optimised set-point temperatures can highly improve heating accuracy, which is less than 1 °C from the desired discharge temperature.