%0 Journal Article %1 Lakshminarayanan:2000:CCE %A Lakshminarayanan, S. %A Fujii, H. %A Grosman, B. %A Dassau, E. %A Lewin, D. R. %D 2000 %J Computers & Chemical Engineering %K PCR PLSR, Product algorithms, design, genetic programming, %N 2-7 %P 671--676 %R doi:10.1016/S0098-1354(00)00406-3 %T New product design via analysis of historical databases %U http://www.sciencedirect.com/science/article/B6TFT-448HNR0-2P/2/eecc15ac2e7cad2e662af963aa783893 %V 24 %X A methodology is presented to define a set of operating conditions to produce a desired product, given a database of historical operating conditions and the product quality that they produced. This approach relies on the generation of a reliable model that can be used to predict the quality variables (the Y block) from the decision variables (the X block). Genetic programming (GP) is used to automatically generate accurate nonlinear models relating latent vectors for the X and Y blocks. The GP has the capability to carry out simultaneous optimisation of model relationship structures and parameters, as well as to identify the most important basis functions. Once an adequate model is generated, it is used to predict the required process conditions to meet the new quality target by reverse mapping.

@article{Lakshminarayanan:2000:CCE, abstract = {A methodology is presented to define a set of operating conditions to produce a desired product, given a database of historical operating conditions and the product quality that they produced. This approach relies on the generation of a reliable model that can be used to predict the quality variables (the Y block) from the decision variables (the X block). Genetic programming (GP) is used to automatically generate accurate nonlinear models relating latent vectors for the X and Y blocks. The GP has the capability to carry out simultaneous optimisation of model relationship structures and parameters, as well as to identify the most important basis functions. Once an adequate model is generated, it is used to predict the required process conditions to meet the new quality target by reverse mapping.}, added-at = {2008-06-19T17:35:00.000+0200}, author = {Lakshminarayanan, S. and Fujii, H. and Grosman, B. and Dassau, E. and Lewin, D. R.}, biburl = {https://www.bibsonomy.org/bibtex/2b6722506628258d7e68a36f7ee3a3f82/brazovayeye}, doi = {doi:10.1016/S0098-1354(00)00406-3}, interhash = {fc1871e4e33a12ac6cdf9ad0dd7f2793}, intrahash = {b6722506628258d7e68a36f7ee3a3f82}, journal = {Computers \& Chemical Engineering}, keywords = {PCR PLSR, Product algorithms, design, genetic programming,}, number = {2-7}, owner = {wlangdon}, pages = {671--676}, timestamp = {2008-06-19T17:44:39.000+0200}, title = {New product design via analysis of historical databases}, url = {http://www.sciencedirect.com/science/article/B6TFT-448HNR0-2P/2/eecc15ac2e7cad2e662af963aa783893}, volume = 24, year = 2000 }