System dynamics modelling and simulation of biogas production systems

1991 ◽  
Vol 1 (5-6) ◽  
pp. 723-728 ◽  
Author(s):  
B.K. Bala
2000 ◽  
Vol 5 (3) ◽  
pp. 149-157 ◽  
Author(s):  
IAN WHALLEY

Based on a composer's psycho-acoustic imagination or response to music, system dynamics modelling and simulation tools can be used as a scoring device to map the structural dynamic shape of interest of computer music compositions. The tools can also be used as a generator of compositional ideas reflecting thematic juxtaposition and emotional flux in musical narratives. These techniques allow the modelling of everyday narratives to provide a structural/metaphorical means of music composition based on archetypes that are shared with wider audiences. The methods are outlined using two examples.


2016 ◽  
Vol 333 ◽  
pp. 51-65 ◽  
Author(s):  
Jeffrey P. Walters ◽  
David W. Archer ◽  
Gretchen F. Sassenrath ◽  
John R. Hendrickson ◽  
Jon D. Hanson ◽  
...  

2016 ◽  
Vol 5 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Erik Pruyt

Although System Dynamics modelling is sometimes referred to as data-poor modelling, it often is –or could be– applied in a data-rich manner. However, more can be done in the era of ‘big data'. Big data refers here to situations with much more available data than was until recently manageable. The field of data science makes big(ger) data manageable. This paper provides a perspective on the future of System Dynamics with a prominent place for bigger data and data science. It discusses different approaches for dealing with bigger data. It reviews methods, techniques and tools for dealing with bigger data in System Dynamics, and sheds light on the modelling phases for which data science is most useful. Finally, it provides several examples of current applications in which big data, data science, and System Dynamics modelling and simulation are being merged.


2012 ◽  
Vol 2012 ◽  
pp. 1-25 ◽  
Author(s):  
K. Agyapong-Kodua ◽  
R. H. Weston ◽  
S. Ratchev

Enterprise modelling techniques support business process (re)engineering by capturing existing processes and based on perceived outputs, support the design of future process models capable of meeting enterprise requirements. System dynamics modelling tools on the other hand are used extensively for policy analysis and modelling aspects of dynamics which impact on businesses. In this paper, the use of enterprise and system dynamics modelling techniques has been integrated to facilitate qualitative and quantitative reasoning about the structures and behaviours of processes and resource systems used by a Manufacturing Enterprise during the production of composite bearings. The case study testing reported has led to the specification of a new modelling methodology for analysing and managing dynamics and complexities in production systems. This methodology is based on a systematic transformation process, which synergises the use of a selection of public domain enterprise modelling, causal loop and continuous simulation modelling techniques. The success of the modelling process defined relies on the creation of useful CIMOSA process models which are then converted to causal loops. The causal loop models are then structured and translated to equivalent dynamic simulation models using the proprietary continuous simulation modelling tool iThink.


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