Experimental data obtained from the operation in a pilot plant are used to perform mass and energy balances to a global process combining units of thermal hydrolysis (TH) of secondary sludge, anaerobic digestion (AD) of hydrolysed secondary sludge together with fresh primary sludge, and cogeneration from biogas by using a gas engine in which the biogas produces electricity and heat from the exhaust gases. Three scenarios were compared, corresponding to the three digesters operated: C (conventional AD, 17 days residence time), B (combined TH + AD, same time), and A (TH + AD at half residence time). The biogas production of digesters B and A was 33 and 24% better, respectively when compared with C. In the case of the combined TH + AD process (scenarios A and B), the key factors in the energy balance were the recovery of heat from hot streams, and the concentration of sludge. The results of the balances showed that for 8% DS concentration of the secondary sludge tested in the pilot plant, the process can be energetically self-sufficient, but a fraction of the biogas must by-pass the gas engine to be directly burned. From an economic point of view, scenario B is more profitable in terms of green energy and higher waste removal, while scenario A reduces the digester volume required by a half. Considering a population of 100,000 inhabitants, the economic benefit is 87,600 €/yr for scenario A and 132,373 €/yr for B. This value can be increased to 223,867 €/yr by increasing the sludge concentration of the feeding to the TH unit to a minimum value that allows use of all the biogas to produce green energy. This concentration is 13% DS, which is still possible from a practical point of view. Additional benefits gained with the combined TH + AD process are the enhancement of the digesters rheology and the possibility of getting Class A biosolids. The integration study presented here set the basis for the scale-up to a demonstration plant.
Low-temperature thermal hydrolysis of sludge prior to anaerobic digestion: principal component analysis (PCA) of experimental data
