Development of a new cleaner production process for cassava ethanol

A new cleaner production process for cassava ethanol has been developed, in which the thin stillage by-product was treated initially by anaerobic digestion, and the digestate further processed by hydrogen-form cation exchange resin before being recycled as process water to make mash for the next ethanol fermentation batch.Thus wastewater was eliminated and freshwater and energy consumption was significantly reduced. To evaluate the new process, ten consecutive batches of ethanol fermentation and anaerobic digestion at lab scale were carried out. Average ethanol production in the recycling batches was 11.43%(v/v) which was similar to the first batch, where deionized(DI) water was used as process water. The chemical oxygen demand(COD) removal rate reached 98% and the methane yield was 322 ml per gram of COD removed, suggesting an efficient and stable operation of the anaerobic digestion. In conclusion, the application of the new process can contribute to sustainable development of the cassava ethanol industry.

The Influences of Concentrate Extract Properties and Ethanol Addition Amount on the Ethanol Precipitation Process of Salvia Miltiorrhiza

In this work, 10 batches of Salvia miltiorrhiza concentrate were prepared and purified with ethanol precipitation process. Dry matter content, pH value, conductivity and water content of the concentrates and supernatants were all determined. When more ethanol was used in ethanol precipitation, the pH value of the supernatant generally increased, but dry matter content, water content, and the conductivity decreased. Multivariate linear models were built with the most determination coefficient values greater than 0.7. More than 80% of stachyose was removed in the ethanol precipitation process. The removal rate of fructose, raffinose and sucrose were all higher than 30%. When ethanol addition amount increased, the purity of phenolic acids in the supernatant increased, but the retention of lithosperimic acid and salvianolic acid B decreased. The conductivity and pH value of concentrated extract show relatively small influences on ethanol precipitation indices. When fructose, raffinose, or stachyose contents in the concentrated extract were high, the retention rate of phenolic acids tends to be low on most occasions. The purity and retention rate of phenolic acids in the supernatants were also affected by the purity of phenolic acids in the concentrated. The sugar contents in the concentrate are suggested to be monitored in industry because they significantly affect ethanol precipitation process indices.

Learning control of fermentation process with an improved DHP algorithm

Control of the fed-batch ethanol fermentation processes to produce maximum product ethanol is one of the key issues in the bioreactor system.However,ethanol fermentation processes exhibit complex behavior and nonlinear dynamics with respect to the cell mass,substrate,feed-rate,etc.An improved dual heuristic programming algorithm based on the least squares temporal difference with gradient correction(LSTDC) algorithm(LSTDC-DHP) is proposed to solve the learning control problem of a fed-batch ethanol fermentation process.As a new algorithm of adaptive critic designs,LSTDC-DHP is used to realize online learning control of chemical dynamical plants,where LSTDC is commonly employed to approximate the value functions.Application of the LSTDC-DHP algorithm to ethanol fermentation process can realize efficient online learning control in continuous spaces.Simulation results demonstrate the effectiveness of LSTDC-DHP,and show that LSTDC-DHP can obtain the near-optimal feed rate trajectory faster than other-based algorithms.