Photofermentation of malate for biohydrogen production— A modeling approach

A kinetic model for photofermentative biohydrogen production is developed in this study to predict the dynamics of the process. The proposed model contains 17 parameters to describe cell growth, substrate consumption, and hydrogen evolution as well as inhibition of the process by biomass, light intensity, and substrate. Batch experimental results from the literature were used to calibrate and validate the model with malic acid as a model substrate, using Rhodobacter sphaeroides as a model biomass. Temporal hydrogen evolution and cell growth predicted by the proposed model agreed well with the experimentally measured data obtained from four literature reports, with statistically significant correlation coefficients exceeding 0.9. Based on sensitivity analysis performed with the validated model, only six of the 17 parameters were found to be significant. Model simulations indicated that the range of optimal light intensity for maximum hydrogen yield from malate by R. sphaeroides was 150–250 W/m².     

Large margin mixture of AR models for time series classification

In this paper, we propose the large margin autoregressive (LMAR) model for classification of time series patterns. The parameters of the generative AR models for different classes are estimated using the margin of the boundaries of AR models as the optimization criterion. Models that use a mixture of AR (MAR) models are considered for representing the data that cannot be adequately represented using a single AR model for a class. Based on a mixture model representing each class, we propose the large margin mixture of AR (LMMAR) models. The proposed methods are applied on the simulated time series data, electrocardiogram data, speech data for E-set in English alphabet and electroencephalogram time series data. Performance of the proposed methods is compared with that of support vector machine (SVM) based classifier that uses AR coefficients based features. The proposed methods give a better classification performance compared to the SVM based classifier. Being generative models, the LMAR and LMMAR models provide a generative interpretation that enables utilization of the rejection option in the high risk classification tasks. The proposed methods can also be used for detection of novel time series data.     

Improving Air-Cooled Condenser Performance in Combined Cycle Power Plants

It has been recognized in recent times that air-cooled condensers (ACCs) are environmentally preferable to the traditional water-cooled condensers for rejecting heat in combined-cycle power plants (CCPPs). However, a drawback of ACCs is that their performance can decline with increasing ambient air temperature. A new approach is proposed in this paper that has the potential to alleviate this drawback of ACCs. In this approach, a chilled-water thermal energy storage system (TES) is used to precool the inflow air to the ACC whenever the ambient air temperature increases above the design air inlet temperature. The temperature of the TES system is maintained by an absorption refrigeration system (ARS) driven by low-quality waste heat from the CCPP. A process model integrating the CCPP with the ARS and the TES has been developed to optimize the volume of the TES. A 500?MW CCPP with steam turbine net output of 170.9?MW was chosen to evaluate the application of this approach for a power plant to be located in southern New Mexico. This analysis showed that a tank volume of 4,500?m3 will be required to maintain the air temperature at the inlet to the ACC at the design value of 20°C throughout the year. Simulations under ambient air temperatures up to 40°C indicated that the proposed system is capable of maintaining the rated net power output of the plant with minimal fluctuations. Simulations also showed that TES tank volume is most sensitive to the design air inlet temperature to the ACC—an increase of this temperature by 1°C can result in at least 25% reduction in the volume.