in Wiley InterScience (www.interscience.wiley.com).The novel feature of the presented simulated moving bed (SMB) controller is its capability to make use of the average outlet concentration of the product streams over a cycle as feedback information, i.e., 'cycle to cycle' control. Its effectiveness is confirmed experimentally on an eight-column four-section laboratory SMB unit, which is used to separate a binary mixture of the nucleosides, uridine and guanosine. The performance of the 'cycle to cycle' SMB control scheme is also demonstrated by several SMB simulation runs that are chosen to test the robustness of the controller. Furthermore, the case where measurements have a time delay is presented. The results illustrate that the 'cycle to cycle' controller is able to meet the products' purity specifications and operate the process optimally with minimal information about the system regardless of the disturbances that might take place during the operation.Note that Y k contains the concentrations of both species for every time step n 5 0, . . ., N 2 1 of cycle k. Figure 4. Performance of the controller on the laboratory SMB plant for case studies 1 and 2. Outlet purities and cost function vs. time measured in cycles. Feed pump delivers 10% more than its set point after cycle 70. [Color figure can be viewed in the online issue, which is available at www.interscience.wiley.com.]

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Abstract-The application of nuclear norm regularization to system identification was recently shown to be a useful method for identifying low order linear models. In this paper, we consider nuclear norm regularization for identification of simulated moving bed processes from data sets with missing entries. The missing data problem is of ongoing interest because the need to analyze incomplete data sets arises frequently in diverse fields such as chemistry, psychometrics and satellite imaging. By casting system identification as a convex optimization problem, nuclear norm regularization can be applied to identify the system in one step, i.e., without imputation of the missing data. Our exploratory work compares the proposed method named NucID to the standard techniques N4SID, prediction error minimization, subspace identification and expectation conditional maximization via linear regression and a linearized first principles model. NucID is found to consistently identify systems with missing data within the imposed error tolerance, a task for which the standard methods sometimes fail, and to be particularly effective when the data is missing with patterns, e.g., on multi-rate systems, where it significantly outperforms existing procedures.

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In order to better exploit the economic potential of the simulated moving bed chromatography a 'cycle to cycle' controller which only requires the information about the linear adsorption behavior and the overall average porosity of the columns has been proposed. Recently, an automated on-line HPLC monitoring system which determines the concentrations in the two product streams averaged over one cycle, and returns them as feedback information to the controller was implemented. The new system allows for an accurate determination of the average concentration of the product streams even if the plant is operated at high concentrations. This paper presents the experimental implementation of the 'cycle to cycle' control concept to the separation of guaifenesin enantiomers under nonlinear chromatographic conditions, i.e. at high feed concentrations. Different case studies have been carried out to challenge the controller under realistic operation conditions, e.g. introducing pump disturbances and changing the feed concentration during the operation. The experimental results clearly demonstrate that the controller can indeed deliver the specified purities and improve the process performance.