The new movement towards green chemistry and renewable feedstocks makes microbial production of chemicals more competitive. Among the numerous chemicals, organic acids are more attractive targets for process development efforts in the renewable-based biorefinery industry. However, most of the production costs in microbial processes are higher than that in chemical processes, among which over 60% are generated by separation processes. Therefore, the research of separation and purification processes is important for a promising biorefinery industry. This review highlights the progress of recovery processes in the separation and purification of organic acids, including their advantages and disadvantages, current situation, and future prospects in terms of recovery yields and industrial application.

Driving patterns exert an important influence on the fuel economy of vehicles, especially hybrid electric vehicles. This paper aims to build a method to identify driving patterns with enough accuracy and less sampling time compared than other driving pattern recognition algorithms. Firstly a driving pattern identifier based on a Learning Vector Quantization neural network is established to analyze six selected representative standard driving cycles. Micro-trip extraction and Principal Component Analysis methods are applied to ensure the magnitude and diversity of the training samples. Then via Matlab/Simulink, sample training simulation is conducted to determine the minimum neuron number of the Learning Vector Quantization neural network and, as a result, to help simplify the identifier model structure and reduce the data convergence time. Simulation results have proved the feasibility of this method, which decreases the sampling window length from about 250-300 s to 120 s with an acceptable accuracy. The driving pattern identifier is further used in an optimized co-simulation together with a parallel hybrid vehicle model and improves the fuel economy by about 8%.

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Aim: Ginsenosides are considered to be the major pharmacologically active ginseng constituents, whereas 20(S)-protopanaxadiol [20(S)-PPD] is the active metabolite of ginsenosides in gut. In this study we investigated the effect of 20(S)-PPD on isolated rat thoracic aortas as well as its vasorelaxant mechanisms. Methods: Aortic rings with or without endothelium were prepared from Wistar rats and suspended in organ-chambers. The changes in tension of the preparations were recorded through isometric transducers connected to a data acquisition system. The aortic rings were precontracted with phenylephrine (PE, 1 µmol/L) or high-K + (80 mmol/L).