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Development of an Application Programming Interface (API) for Biomass Feedstock Production Engineering
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.orgCitation: 2010 Pittsburgh, Pennsylvania, June 20 - June 23, 2010 1009678.(doi:10.13031/2013.29978)
Authors: Konstantinos Domdouzis, Luis Rodriguez, Alan Hansen, K.C Ting
Keywords: Biomass Feedstock Production, Application Programming Interface, Visualization, Metadata, Artificial Intelligence, Regression Analysis
Biomass feedstock production is an integral element of bioenergy production. The amount of useful information distributed among various stages of biomass feedstock production is large while there is increased complexity in the relationship between the generated data. A systems informatics infrastructure is therefore considered necessary in order for the people involved in biomass feedstock production to be able to access the generated data, exchange data amongst each other, and also manipulate them. The ultimate goal of this infrastructure is the provision of effective procedures for decision making in a concurrent way. Specifically, all the stages of biomass feedstock production should be able to handle data simultaneously. Furthermore, any knowledge associated with the infrastructure must be managed efficiently and this can be achieved by the use of the appropriate software engineering techniques. The use of such techniques allows the identification of the requirements for the biomass feedstock production supply chains and the design of an efficient informatics platform based on these requirements. This paper aims to introduce the application programming interface (API) which is the core element of the informatics infrastructure developed for the biomass feedstock production. The API is connected to a database containing data related to each stage of biomass feedstock production. The API provides a number of capabilities to its users, such as the access to and visualization of both existing data and simulation results for analysis purposes, the provision of a metadata-based database search engine, the identification of hidden relationships between data through the use of data clustering algorithms, and the identification of the strength of these relationships through the use of rule-based techniques. Furthermore, the capabilities of the API will expand to the field of artificial intelligence and especially artificial neural networks and genetic algorithms for the realization of predictions and optimization. Regression analysis is another technique which will be provided by the API to facilitate the exploration of optimal values of data which affect other data related to biomass feedstock production to be explored.