An updated comprehensive techno-economic analysis of algae biodiesel

Algae biodiesel is a promising but expensive alternative fuel to petro-diesel. To overcome cost barriers, detailed cost analyses are needed. A decade-old cost analysis by the U.S. National Renewable Energy Laboratory indicated that the costs of algae biodiesel were in the range of $0.53–0.85/L (2012 USD values). However, the cost of land and transesterification were just roughly estimated. In this study, an updated comprehensive techno-economic analysis was conducted with optimized processes and improved cost estimations. Latest process improvement, quotes from vendors, government databases, and other relevant data sources were used to calculate the updated algal biodiesel costs, and the final costs of biodiesel are in the range of $0.42–0.97/L. Additional improvements on cost-effective biodiesel production around the globe to cultivate algae was also recommended. Overall, the calculated costs seem promising, suggesting that a single step biodiesel production process is close to commercial reality.

Advanced Business Simulations-Incorporating business and process execution data

Key Performance Indicators (KPIs) and their predictions are widely used by the enterprises for informed decision making. Nevertheless , a very important factor, which is generally overlooked, is that the top level strategic KPIs are actually driven by the operational level business processes. These two domains are, however, mostly segregated and analysed in silos with different Business Intelligence solutions. In this paper, we are proposing an approach for advanced Business Simulations, which converges the two domains by utilising process execution & business data, and concepts from Business Dynamics (BD) and Business Ontologies, to promote better system understanding and detailed KPI predictions. Our approach incorporates the automated creation of Causal Loop Diagrams, thus empowering the analyst to critically examine the complex dependencies hidden in the massive amounts of available enterprise data. We have further evaluated our proposed approach in the context of a retail use-case that involved verification of the automatically generated causal models by a domain expert.