GASDS: A kinetic-based package for biomass and coal gasification

In this paper, a simulation package called GASDS is introduced. It is particularly suited to evaluate the pyrolysis, gasification and combustion of biomass and coal feedstocks. The aim of this work is to describe the package from a numerical point of view and its interface. Additionally, experimental results for a countercurrent fixed-bed biomass gasification reactor are reproduced. The influence of reactor and particle discretizations are investigated with respect to accuracy and computational time. Some differences are present between experimental and simulation results. In order to improve the agreement between simulation and experimental results it is suggested to improve the kinetic scheme of the solid phase and gas-solid reactions. The negligible differences in terms of predictions, instead, do not justify the adoption of finer discretizations for the particle and reactor, which imply longer computational times

Towards Online Model Predictive Control on a Programmable Logic Controller: Practical Considerations

Given the growing computational power of embedded controllers, the use of model predictive control (MPC) strategies on this type of devices becomes more and more attractive. This paper investigates the use of online MPC, in which at each step, an optimization problem is solved, on both a programmable automation controller (PAC) and a programmable logic controller (PLC). Three different optimization routines to solve the quadratic program were investigated with respect to their applicability on these devices. To this end, an air heating setup was built and selected as a small-scale multi-input single-output system. It turns out that the code generator (CVXGEN) is not suited for the PLC as the required programming language is not available and the programming concept with preallocated memory consumes too much memory. The Hildreth and qpOASES algorithms successfully controlled the setup running on the PLC hardware. Both algorithms perform similarly, although it takes more time to calculate a solution for qpOASES. However, if the problem size increases, it is expected that the high number of required iterations when the constraints are hit will cause the Hildreth algorithm to exceed the necessary time to present a solution. For this small heating problem under test, the Hildreth algorithm is selected as most useful on a PLC

A roadmap for in silico development and evaluation of industrial NMPC applications: A practical case study

Linear Model Predictive Control (MPC) can be considered as the state of the art advanced process control technology in model-based automation of continuous chemical processes. For (semi-)batch processes, that often present a strongly nonlinear (or even unstable) behavior in combination with fast dynamics, Nonlinear Model Predictive Control (NMPC) is a more suited technology. However, online applications of NMPC have a hard time to penetrate in industry despite methodological developments, tools and examples in academia. In this paper, we propose a roadmap to argue against the intrinsic reasons and practical limitations that slow down the practical online applications of NMPC. This roadmap is applied to an existing semi-batch plant as a practical case study. The results have shown that the NMPC algorithm can provide an improved control, namely a better tracking of the main process variables, a reduction in the reaction time, and robustness with respect to model-plant mismatch and disturbances

Including experimental uncertainty on the independent variables when modelling microbial dynamics: The combined effect of pH and acetic acid on the growth rate of E. coli K12

Modelling methods applied in predictive microbiology generally neglect the importance of uncertainty on the measurement of the independent variables. The Ordinary Least Squares (OLS) method that is commonly applied in predictive microbiology is only applicable if the experimental error on the inputs of the model are insignificant. However, this does not apply for many types of experimental measurements of the independent variables. Therefore, a parameter estimation method was adapted in this research for the estimation of the parameters of secondary models, taking into account uncertainty on the measurement of the influencing food characteristics. This parameter estimation method was based on the work of Stortelder (1996) and is referred to as the Weighted Total Least Squares method (WTLS). The method is formulised as an extension of the commonly used OLS method. Consequently the current WTLS method (i) is easily implemented using similar numerical methods, (ii) reduces to an OLS method when the measurement error on the model inputs is negligible and (iii) enables the evaluation of the accuracy of the model parameter estimates based on the same approximations.status: publishe

Optimal experimental design for discriminating between microbial growth models as function of suboptimal temperature.

International audienceIn the field of predictive microbiology, mathematical models play an important role for describing microbial growth, survival and inactivation. Often different models are available for describing the microbial dynamics in a similar way. However, the model that describes the system in the best way is desired. Optimal experimental design for model discrimination (OED-MD) is an efficient tool for discriminating among rival models. In this work the T12-criterion proposed by Atkinson and Fedorov (1975) [1] and applied efficiently by Ucinski and Bogacka (2005) [2] and the Schwaab-approach proposed by Schwaab et al. (2008) [3] and Donckels et al. (2009) [4] will be applied for discriminating among rival models for the microbial growth rate as a function of temperature. The two methods will be tested in silico and their performances will be compared. Results from a simulation study indicate that it is possible to validate the case that one of the proposed models is more accurate for describing the temperature effect on the microbial growth rate. Both methods are able to design inputs with a sufficient discrimination potential. However, it has been observed that the Schwaab-approach provides inputs with a higher discrimination potential in combination with more accurate parameter estimates