01474nas a2200217   4500008004100000022001400041245010800055210006900163260009500232300001200327490000700339520063300346653002300979653002701002653001601029653003001045100001301075700001501088700002101103856013201124       2007                        eng d  a0263-876200aDevelopment of a soft sensor for a batch distillation column using support vector regression techniques0 aDevelopment of a soft sensor for a batch distillation column usi  a165-189 RAILWAY TERRACE, DAVIS BLDG, RUGBY CV21 3HQ, ENGLANDbINST CHEMICAL ENGINEERScFEB  a283-2870 v853 a<p>A support vector regression (SVR)-based model is developed for a batch distillation process in order to estimate the product compositions from temperature measurements. Kernel function such as linear, polynomial and RBF are employed for SVR modelling. The original process data was generated by simulating the batch distillation process, varying the initial feed composition and boilup rate from batch to batch. Within each batch reflux ratio was also randomly changed to represent the true dynamics of the batch distillation. The results show the potential of the method for developing softsensor for chemical processes.</p>10aBatch distillation10acomposition estimation10asoft sensor10asupport vector regression1 aJain, P.1 aRahman, I.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/development-soft-sensor-batch-distillation-column-using-support-vector-regression-techniques01508nas a2200169   4500008004100000245007900041210006900120260000800189300001200197490000700209520094700216100001401163700001501177700002101192700001501213856011001228       2016                        eng d00aRecursive orthogonal least square based soft sensor for batch distillation0 aRecursive orthogonal least square based soft sensor for batch di  cSEP  a241-2630 v113 aA multiple-input and multiple-output (MIMO) model, namely Recursive Orthogonal Least Square (ROLS) based radial basis function (RBF) is developed to estimate product compositions in a batch distillation process from temperature measurements. The process data is generated by simulating the differential equations of the batch distillation process, changing the initial feed composition and boiluprate from batch to batch. Moreover, the reflux ratio is also randomly varied within each batch to represent the exact dynamics of the batch distillation. Temperature and distillate composition is correlated by the RBF trained by ROLS algorithm. A Single RBF network estimate the quality of products in real-time. The results show that ROLS based estimator give correct composition estimations for a batch distillation process. The robustness of the ROLS algorithm and low computational requirement makes the estimator attractive for on-line use. 1 aMehta, S.1 aRamani, H.1 aYelgatte, N., N.1 aRahman, I.  uhttps://library.ncl.res.in/content/recursive-orthogonal-least-square-based-soft-sensor-batch-distillation01393nas a2200145   4500008004100000022001300041245013600054210006900190260000800259490000700267520081200274100001701086700001501103856012901118       2018                        eng d  a2194615900aOptimization of pressure-Swing distillation by evolutionary techniques: separation of ethanol-water and acetonitrile-water mixtures0 aOptimization of pressureSwing distillation by evolutionary techn  cJUN0 v133 aComplete separation can be achieved in selective homogeneous azeotropic mixtures by exploiting the pressure sensitive nature of the system. In the present work the optimal number of trays, feed location and reflux ratio for sequential column systems encountered in continuous pressure swing distillation (PSD) have been determined by use of two evolutionary techniques. Two industrially relevant systems: ethanol-water and acetonitrile-water have been considered. The Napthali-Sandholm model is solved to obtain the concentration and temperature profiles. The objective is to minimize the total cost using Genetic Algorithm (GA) and Differential Evolution (DE) for the two azeotropic systems. The techniques offer attractive features like applicability to discontinuous and non-differentiable search spaces.1 aSagar, Adjay1 aRahman, I.  uhttps://library.ncl.res.in/content/optimization-pressure-swing-distillation-evolutionary-techniques-separation-ethanol-water01333nas a2200133   4500008004100000245009000041210006900131260000800200520082600208100001501034700001501049700001501064856012001079       2018                        eng d00aRecursive orthogonal least squares based adaptive control of a polymerisation reactor0 aRecursive orthogonal least squares based adaptive control of a p  cNOV3 aA Nonlinear Internal Model Control (NIMC) scheme, based on a recursively updated Radial Basis Function Network (RBFN), is applied to control the nonlinear Polymerisation process. Recursive Orthogonal Least Squares (ROLS) algorithm recursively updates the weighting matrix of RBFN in real time, such that modelling errors are minimised. In addition to the ROLS based adaptive IMC, a non-adaptive IMC, based on the fixed Orthogonal Least Squares (OLS) algorithm and a normal PID controller are also applied to control the polymerisation process. The simulated results show the effectiveness of the ROLS based controller in terms of response time to step changes and disturbance rejection capabilities over the classical PID controller and the fixed OLS based IMC. Also, the proposed algorithm fits well in the IMC framework1 aTiwari, S.1 aSawant, P.1 aRahman, I.  uhttps://library.ncl.res.in/content/recursive-orthogonal-least-squares-based-adaptive-control-polymerisation-reactor