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Software Sensors and Their Applications in Bioprocess

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Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control

Part of the book series: Studies in Computational Intelligence ((SCI,volume 218))

Abstract

Industrial bioprocesses present a very difficult challenge to control engineers. Problems associated with the nature of the organisms in the process and difficulties related to obtaining accurate information regarding the progression of the process make controlling and monitoring particularly challenging. The lack of suitable and robust on-line sensors for key variables such as biomass or product concentration has been considered as a serious obstruction for the implementation of control and optimization of bioprocesses. Considering biomass concentration alone, there are typically two methods available to measure this value - direct or indirect methods. To measure the biomass directly, several techniques have been applied: optical density measurements, capacity measurements, high performance liquid chromatography (HPLC), nuclear magnetic resonance (NMR), laser cytometry or biosensors. In addition to the high costs associated with these measuring devices, their reliability can be poor when applied to large-scale systems. It is still the case that most industrial bioprocess control policies are based upon the use of infrequent off-line assay information for process operator supervision. The low sampling frequency associated with such measurements and the inevitable delays in taking samples and performing laboratory tests inevitably compromises the quality of control that is possible using such measurements. As a result of this an alternative approach, that of indirect measurement has attracted a great deal of attention over the last 20 years or so. Indirect measurements of biomass are mathematical algorithms that can produce estimates of unmeasured biomass concentration using the continuously measured variables such as temperature, dissolved oxygen, pH and off-gas concentration. The method of estimating the quality related variables from measurements of secondary variables is referred to as ‘Inferential Estimation’ and these mathematical estimators are usually referred to as ‘Software Sensors’. Improved control of the process can be achieved by measuring and setting up a feedback control system using these secondary variables. Such control strategies are referred to as ‘Inferential Controllers’. Software sensors usually rely on a model to describe the process, thus different techniques have been proposed for on-line inferential estimation in bioprocesses just as different models exist. Among these applications, the majority have been based upon mechanistic, artificial neural network (ANN) or other empirical models. In this chapter, some of the important and recent research conducted on Software Sensors is reviewed and the associated techniques are introduced with examples and case studies.

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Authors and Affiliations

  1. Faculty of Arts, Computing, Engineering and Science, Sheffield Hallam University, City Campus, Sheffield, UK, S1 1WB

    Hongwei Zhang

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  1. Hongwei Zhang
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Editors and Affiliations

  1. CS Department, Universidade Federal de S. Carlos, Rod.Washington Luiz, km235, Caixa Postal 676, 13565-905, S. Carlos, SP, Brazil

    Maria do Carmo Nicoletti

  2. School of Electrical and Information Engineering, University of South Australia, Mawson Lakes Campus, 5095, Adelaide, SA, Australia

    Lakhmi C. Jain

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Zhang, H. (2009). Software Sensors and Their Applications in Bioprocess. In: do Carmo Nicoletti, M., Jain, L.C. (eds) Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control. Studies in Computational Intelligence, vol 218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01888-6_2

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  • DOI: https://doi.org/10.1007/978-3-642-01888-6_2

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