Evaluation of smoothing techniques in the run to run optimization of fed‐batch processes with u‐PLS

Abstract

Run to run (R2R) optimization based on unfolded Partial Least Squares (u-PLS) is a promising approach for improving the performance of batch and fed-batch processes as it is able to continuously adapt to changing processing conditions. Using this technique, the regression coefficients of PLS are used to modify the input profile of the process in order to optimize the yield. When this approach was initially proposed, it was observed that the optimization performed better when PLS was combined with a smoothing technique, in particular a sliding window filtering, which constrained the regression coefficients to be smooth. In the present paper, this result is further investigated and some modifications to the original approach are proposed. Also, the suitability of different smoothing techniques in combination with PLS is studied for both end-of-batch quality prediction and R2R optimization. The smoothing techniques considered in this paper include the original filtering approach, the introduction of smoothing constraints in the PLS calibration (Penalized PLS), and the use of functional analysis (Functional PLS). Two fed-batch process simulators are used to illustrate the results.

Multivariate batch to batch optimisation of fermentation processes incorporating validity constraints

“…In [21,30,31], it was suggested that the identified MVT be passed through a low-pass filter before being applied to the process. In this work, filtering the identified MVT proved to be beneficial and hence in each case study the MVT was passed through a zero-phase, low pass Finite Impulse…”

Networkmetrics: multivariate big data analysis in the context of the internet

“…A form of pseudo‐optimization in this class, which seeks to minimize the number of measurement pairs in x and y , is the design of experiments , central in chemometrics. Another typical example of data‐driven optimization is that used for process optimization, for instance in run‐to‐run optimization (see and references therein).…”

“…The relay placement problem in ad‐hoc networks can be tackled with heuristic optimizations. In particular, Figure shows a comparison between PSO and the PLS optimization algorithm proposed in . This comparison was performed in a simulator based on MATLAB .…”

“…Comparison in terms of connectivity of two optimization algorithms in the problem of relay node placement in a mobile ad‐hoc network. The algorithms are the p article swarm optimization (PSO) and the partial least squares (PLS) optimization algorithm of .…”

“…In a similar way, Experimental Design can be applied in optimization problems for fixed topology networks, especially when deciding the number and location of network elements. On the other hand, considering the cyclostationarity of most networks, the batch‐processing parallelism also holds in optimization, and techniques applied to optimize batch processes can be applied to network optimization. In particular, mid‐course corrections are suitable for the daily optimization operations typical in traffic engineering.…”