scholarly journals Dynamic Optimisation of Beer Fermentation under Parametric Uncertainty

Fermentation ◽  
2021 ◽  
Vol 7 (4) ◽  
pp. 285
Author(s):  
Satyajeet Bhonsale ◽  
Wannes Mores ◽  
Jan Van Impe

Fermentation is one of the most important stages in the entire brewing process. In fermentation, the sugars are converted by the brewing yeast into alcohol, carbon dioxide, and a variety of by-products which affect the flavour of the beer. Fermentation temperature profile plays an essential role in the progression of fermentation and heavily influences the flavour. In this paper, the fermentation temperature profile is optimised. As every process model contains experimentally determined parameters, uncertainty on these parameters is unavoidable. This paper presents approaches to consider the effect of uncertain parameters in optimisation. Three methods for uncertainty propagation (linearisation, sigma points, and polynomial chaos expansion) are used to determine the influence of parametric uncertainty on the process model. Using these methods, an optimisation formulation considering parametric uncertainty is presented. It is shown that for the non-linear beer fermentation model, the linearisation approach performed worst amongst the three methods, while second-order polynomial chaos worked the best. Using the techniques described below, a fermentation process can be optimised for ensuring high alcohol content or low fermentation time while ensuring the quality constraints. As we explicitly consider uncertainty in the process, the solution, even though conservative, will be more robust to parametric uncertainties in the model.

Water ◽  
2021 ◽  
Vol 13 (13) ◽  
pp. 1830
Author(s):  
Gullnaz Shahzadi ◽  
Azzeddine Soulaïmani

Computational modeling plays a significant role in the design of rockfill dams. Various constitutive soil parameters are used to design such models, which often involve high uncertainties due to the complex structure of rockfill dams comprising various zones of different soil parameters. This study performs an uncertainty analysis and a global sensitivity analysis to assess the effect of constitutive soil parameters on the behavior of a rockfill dam. A Finite Element code (Plaxis) is utilized for the structure analysis. A database of the computed displacements at inclinometers installed in the dam is generated and compared to in situ measurements. Surrogate models are significant tools for approximating the relationship between input soil parameters and displacements and thereby reducing the computational costs of parametric studies. Polynomial chaos expansion and deep neural networks are used to build surrogate models to compute the Sobol indices required to identify the impact of soil parameters on dam behavior.


2013 ◽  
Vol 805-806 ◽  
pp. 281-285
Author(s):  
Zhong Xu

Bioconversion of potato pulp to fuel ethanol, analysing the potato pulp chemical composition and determining the potato pulp in the role of microorganism produce ethanol under the best conditions is the major research. An analysis of the chemical composition of potato pulp showed that : the basic ingredients are Protein (9.72%), Starch (25.52%), Cellulose (17.90%). The effects of ethanol production rate of solid-liquid ratio, fermentation temperature, inoculumconcertration, fermentation time. The results showed that: the best conditions producting ethanol from potato pulp obtained by single factor experiments are: solid-liquid ratio: 1:15, fermentation temperature: 35°C, inoculumconcertration: 3mL, fermentation time: 20h. Under this occasion, the ethanol production rate was 0.183mL·g-1.


Author(s):  
Alessandra Cuneo ◽  
Alberto Traverso ◽  
Shahrokh Shahpar

In engineering design, uncertainty is inevitable and can cause a significant deviation in the performance of a system. Uncertainty in input parameters can be categorized into two groups: aleatory and epistemic uncertainty. The work presented here is focused on aleatory uncertainty, which can cause natural, unpredictable and uncontrollable variations in performance of the system under study. Such uncertainty can be quantified using statistical methods, but the main obstacle is often the computational cost, because the representative model is typically highly non-linear and complex. Therefore, it is necessary to have a robust tool that can perform the uncertainty propagation with as few evaluations as possible. In the last few years, different methodologies for uncertainty propagation and quantification have been proposed. The focus of this study is to evaluate four different methods to demonstrate strengths and weaknesses of each approach. The first method considered is Monte Carlo simulation, a sampling method that can give high accuracy but needs a relatively large computational effort. The second method is Polynomial Chaos, an approximated method where the probabilistic parameters of the response function are modelled with orthogonal polynomials. The third method considered is Mid-range Approximation Method. This approach is based on the assembly of multiple meta-models into one model to perform optimization under uncertainty. The fourth method is the application of the first two methods not directly to the model but to a response surface representing the model of the simulation, to decrease computational cost. All these methods have been applied to a set of analytical test functions and engineering test cases. Relevant aspects of the engineering design and analysis such as high number of stochastic variables and optimised design problem with and without stochastic design parameters were assessed. Polynomial Chaos emerges as the most promising methodology, and was then applied to a turbomachinery test case based on a thermal analysis of a high-pressure turbine disk.


2002 ◽  
Vol 108 (4) ◽  
pp. 410-415 ◽  
Author(s):  
Tomáš Brányik ◽  
António Vicente ◽  
José Machado Cruz ◽  
José Teixeira

2011 ◽  
Vol 183-185 ◽  
pp. 1273-1277
Author(s):  
Zhong Xu ◽  
Nan Liu ◽  
Dan Zhao ◽  
Duo Wang

Potato starch residue was used as raw material , a single factor test was used to determine the lactobacillus casei L-lactic fermentation in the amounts of CaCO3 addition, fermentation temperature, residue saccharification of starch concentration, the optimal dosage range of fermentation time. With 4 factors and 3 levels of 4 orthogonal test of L-lactic acid by fermentation. The order was: the fermentation temperature> saccharification concentration> fermentation period >CaCO3 dosage. Optimization was as follow : residue saccharification of starch concentration was 80g/L, fermentation temperature was 37°C, CaCO3 addition level was 60g/L, fermentation time was of 60h. Fermentation conditions for this verification test, L-lactic acid content was 72.3g/L, compared with 15.1% before optimization.


2010 ◽  
Vol 25 (12) ◽  
pp. 1608-1618 ◽  
Author(s):  
Umamaheswara Konda ◽  
Tarunraj Singh ◽  
Puneet Singla ◽  
Peter Scott

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