An approach to the selection of optimal sensor locations in distributed parameter systems

To enhance energy production from methane or resource recovery from digestate, anaerobic digestion processes require advanced instrumentation and control tools. Over the years, research on these topics has evolved and followed the main fields of application of anaerobic digestion processes: from municipal sewage sludge to liquid—mainly industrial—then municipal organic fraction of solid waste and agricultural residues. Time constants of the processes have also changed with respect to the treated waste from minutes or hours to weeks or months. Since fast closed loop control is needed for short time constant processes, human operator is now included in the loop when taking decisions to optimize anaerobic digestion plants dealing with complex solid waste over a long retention time. Control objectives have also moved from the regulation of key variables—measured on-line—to the prediction of overall process performance—based on global off-line measurements—to optimize the feeding of the processes. Additionally, the need for more accurate prediction of methane production and organic matter biodegradation has impacted the complexity of instrumentation and should include a more detailed characterization of the waste (e.g., biochemical fractions like proteins, lipids and carbohydrates) and their bioaccessibility and biodegradability characteristics. However, even if in the literature several methodologies have been developed to determine biodegradability based on organic matter characterization, only a few papers deal with bioaccessibility assessment. In this review, we emphasize the high potential of some promising techniques, such as spectral analysis, and we discuss issues that could appear in the near future concerning control of AD processes.

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Instrumentation and control of anaerobic digestion processes: a review and some research challenges

There is a large variety of methods in literature for process design and control, which can be classified into two main categories. The methods in the first category have a sequential approach in which, the control system is designed, only after the details of process design are decided. However, when process design is fixed, there is little room left for improving the control performance. Recognizing the interactions between process design and control, the methods in the second category integrate some control aspects into process design. With the aim of providing an exploration map and identifying the potential areas of further contributions, this paper presents a thematic review of the methods for integration of process design and control. The evolution paths of these methods are described and the advantages and disadvantages of each method are explained. The paper concludes with suggestions for future research activities.

/pdf/integration-of-process-design-and-control-a-review-3kmrbq033k.pdf

Integration of process design and control: A review

This paper presents a new approach for sensor location for state and parameter estimation for stable nonlinear systems. The unique feature of this technique is that sensor locations for state estimation and measurement locations for parameter estimation can be determined within the same framework. To compute optimal sensor locations, information derived from observability covariance matrices is combined with already existing measures, which were proposed either for state or parameter estimation, to compute the degree of observability of a nonlinear system over an operating region. The optimal sensor locations then correspond to the configuration that returns the highest value of the measure for the degree of observability of a system. The proposed method is illustrated in case studies where optimal sensor locations for state and parameter estimation for a binary distillation column and a fixed-bed reactor are computed. The results obtained from the presented approach are compared with a technique based up...

Determining Optimal Sensor Locations for State and Parameter Estimation for Stable Nonlinear Systems

https://repositorium.sdum.uminho.pt/bitstream/1822/138/1/controlengpractice.pdf

Tuning of observer-based estimators: theory and application to the on-line estimation of kinetic parameters

On modelling, monitoring and control of fixed bed bioreactors

Modelling, identification and control of a denitrifying biofilter

This paper deals with the design of adaptive linearizing controllers of process components at the output of fixed bed reactors. The control design is based on the distributed parameter model of the process (late lumping). It takes advantage of the well-known part of the (nonlinear) structure (basically the hydrodynamics) and does not require the knowledge of the kinetics, which are estimated on-line. Two examples are considered: the control of the concentration of one process component and of the gas production rate.

https://ieeexplore.ieee.org/iel4/5816/16225/00751754.pdf

J.P. Babary

Papers

On modelling, monitoring and control of fixed bed bioreactors

Modelling, identification and control of a denitrifying biofilter

Design of adaptive linearizing controllers for fixed bed reactors

Multivariable Long-Range Predictive Control Algorithm Applied to a Continuous Flow Fermentation Process

Influence of the Sensor Location on the Practical Observability of a Fixed Bed Bioreactor