Model optimization of SVM for a fermentation soft sensor

Liu, Guohai; Zhou, Dawei; Xu, Haixia; Mei, Congli

doi:10.1016/j.eswa.2009.08.008

Cited by 70 publications

(27 citation statements)

References 22 publications

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“…Since then, several amendments were made to turn the initial idea of genetic algorithms to more efficient optimization algorithms, very useful information can be found in Michalewicz [20]. In the recent years, GA method has been applied to optimization of many of biotechnology and biochemical engineering processes [21][22][23][24][25].…”

Section: Effect Of Bacteria Od On Sulfur Selectivitymentioning

confidence: 99%

Optimization of biological sulfide removal in a CSTR bioreactor

Roosta

Jahanmiri

Mowla

et al. 2012

Bioprocess Biosyst Eng

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In this study, biological sulfide removal from natural gas in a continuous bioreactor is investigated for estimation of the optimal operational parameters. According to the carried out reactions, sulfide can be converted to elemental sulfur, sulfate, thiosulfate, and polysulfide, of which elemental sulfur is the desired product. A mathematical model is developed and was used for investigation of the effect of various parameters on elemental sulfur selectivity. The results of the simulation show that elemental sulfur selectivity is a function of dissolved oxygen, sulfide load, pH, and concentration of bacteria. Optimal parameter values are calculated for maximum elemental sulfur selectivity by using genetic algorithm as an adaptive heuristic search. In the optimal conditions, 87.76% of sulfide loaded to the bioreactor is converted to elemental sulfur.

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Section: Effect Of Bacteria Od On Sulfur Selectivitymentioning

confidence: 99%

Optimization of biological sulfide removal in a CSTR bioreactor

Roosta

Jahanmiri

Mowla

et al. 2012

Bioprocess Biosyst Eng

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“…is the so-called kernel function that satisfies Mercer's condition [17,18]. Only a certain number of coefficients (a i -a Ã i ) are non-zero, and the corresponding vectors are called support vectors which contribute to the final solution.…”

Section: Brief Review Of Svmmentioning

confidence: 99%

Prediction of the Profit Function for Industrial 2‐Keto‐L‐Gulonic Acid Cultivation

Cui

Jia³

et al. 2011

Chem Eng & Technol

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The profit function is the generic criterion to describe the cost effect of a batch process. To focus on the prediction of the profit function for 2-keto-L-gulonic acid (2-KGA) cultivation, which is potentially applicable for process monitoring and optimal scheduling, rolling learning-prediction (RLP) based on a support vector machine (SVM) is applied. The RLP implies that the SVM training database is rolling updated as the batch of current interest proceeds, and the SVM learning is then repeated for the prediction. The database is further updated after termination of a batch. The updating procedures are investigated in detail. Pseudo-online prediction is carried out using the data from industrial-scale 2-KGA cultivation under actual and hypothetical inoculation sequences. The results indicate that the average relative prediction error is less than 5 % in the later phase of fermentation in all inoculation sequences. Introduction2-Keto-L-gulonic acid (2-KGA) is the precursor for the synthesis of L-ascorbic acid. It is typically produced by a mixed culture of Bacillus megaterium (B. megaterium) and Gluconobacter oxydans (G. oxydans) with L-sorbose as substrate [1][2][3]. In 2-KGA fermentation workshops, batch-to-batch variation in process performance is often observed, even though the same culture conditions are applied. This may result from many factors such as fluctuations in the seed quality and raw materials composition. For control engineers, batch-dependent variation between batches implies the potential to improve the process performance by dynamic optimization or optimal scheduling [4][5][6][7][8]. The profit function, which is defined as the gross profit of a batch over the production time, reflects the cost effect of the batch process and can be used as the index to evaluate the batch performance. Predicting the profit function will benefit process monitoring and optimal scheduling. Here, the focus is on the data-driven prediction of the profit function.In recent years, computational intelligence techniques such as artificial neural networks (ANNs) [9][10][11][12][13][14] and support vector machine (SVM) [14][15][16][17][18][19] received more and more attention because of their ability to model nonlinear systems. SVM, developed by Vapnik, is based on the statistical theory [20,21]. It implements the structural risk minimization (SRM) principle which is an approach to minimize the upper bound risk related to the generalization performance. The solid theoretical basis makes it advantageous in generalization and convergence [19][20][21]. In this work, rolling learning-prediction (RLP) based on an SVM is practiced to predict the profit function of industrial-scale 2-KGA cultivation. The RLP mechanism is adopted to deal with the time-variant property of fermentation, and it makes full use of the available data of the predicted batch by adding these real-time data into the training database of SVM to start a new round of SVM learning. When the prediction approach is online operated, new batches will arrive in ...

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“…Soft sensor applications similar to the cement fineness application address situations where important process variables are available only from off-line laboratory tests and are therefore unavailable for use in control algorithms. This is the case in fermentation process as is presented in [14,15], also this approach can be used for emission prediction and control for a gasoline engine [16]. In [17,18] soft sensors are utilized to provide information of process variables used in steam quality control since online measurement of process variables of interest is not always accurate or reliable and is therefore supplemented by lab analysis taken infrequently in a manual manner.…”

Section: Introductionmentioning

confidence: 98%