Xuejun Liu

The progress of bio-hydrogen technology has led to the development of new energy technologies and is significant for the sustainable use of energy. After summarizing current research results, this study discusses that the key to increasing the hydrogen production rate is to improve the activity of hydrogen producing bacteria under the conditions of anaerobic fermentation. Using waste to prepare hydrogen producing bacteria is the developmental trend. The primary factors influencing bio-hydrogen production from plant straw fermentation are also pointed out, indicating the method to improve the hydrogen production rate from plant straw. In addition, application of artificial intelligence technology to a bio-hydrogen production reactor is helpful to achieve automatic control of continuous bio-hydrogen production and improve the rate of hydrogen production.

Abstract This paper reports an investigation on the accuracy of grid-based routing algorithms used in hydrological models. A quantitative methodology has been developed for objective and data-independent assessment of errors generated from the algorithms that extract hydrological parameters from gridded DEM. The generic approach is to use artificial surfaces that can be described by a mathematical model, thus the ‘true’ output value can be pre-determined to avoid uncertainty caused by uncontrollable data errors. Four mathematical surfaces based on an ellipsoid (representing convex slopes), an inverse ellipsoid (representing concave slopes), saddle and plane were generated and the theoretical ‘true’ value of the Specific Catchment Area (SCA) at any given point on the surfaces could be computed using mathematical inference. Based on these models, tests were made on a number of algorithms for SCA computation. The actual output values from these algorithms on the convex, concave, saddle and plane surfaces were compared with the theoretical ‘true’ values, and the errors were then analysed statistically. The strengths and weaknesses of the selected algorithms are also discussed.

Slope and aspect are the most frequently used surface geomorphic parameters in terrain analysis. While derived from grid DEM, the parameters often display noticeable errors due to errors (a) in data, (b) inherent in data structure, and (c) created by algorithms. It has been observed that some controversial results were reported in evaluating the results by various slope and aspect algorithms, largely because of the variety in assessment methodology and the difficulties in separating errors in data and those generated by the algorithms. This paper reports the study that assesses and compares the results from numerous grid-based slope and aspect algorithms using an analytical approach. Tests were made based on artificial polynomial surfaces which can be defined by mathematical formulae, with controllable “added” data errors. By this approach, different algorithms were quantitatively tested and their error components were analyzed. Thus, their suitability and tolerance related to DEM data characteristics can be described.