Browsing by Subject "Genetic algorithm"

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Bi-objective optimization of nutrient intake and performance of broiler chickens using Gaussian process regression and genetic algorithm
(2023) Ahmadi, Hamed; Rodehutscord, Markus; Siegert, Wolfgang
This study investigated whether quantifying the trade-off between the maxima of two response traits increases the accuracy of diet formulation. To achieve this, average daily weight gain (ADG) and gain:feed ratio (G:F) responses of 7–21-day-old broiler chickens to the dietary supply of three nutrients (intake of digestible glycine equivalents, digestible threonine, and total choline) were modeled using a newly developed hybrid machine learning-based method of Gaussian process regression and genetic algorithm. The dataset comprised 90 data lines. Model-fit-criteria indicated a high model adjustment and no prediction bias of the models. The bi-objective optimization scenarios through Pareto front revealed the trade-off between maximized ADG and maximized G:F and provided information on the needed input of the three nutrients that interact with each other to achieve the trade-off scenarios. The trade-off scenarios followed a nonlinear pattern. This indicated that choosing target values intermediate to maximized ADG and G:F after single-objective optimization is less accurate than feed formulation after quantifying the trade-off. In conclusion, knowledge of the trade-off between maximized ADG and maximized G:F and the needed nutrient inputs will help feed formulators to optimize their feed with a more holistic approach.
Entwicklung von datengetriebenen Auswerteverfahren zur Analyse und Schätzungder Reaktorleistung von Biogasanlagen
(2020) Beltramo, Tanja; Hitzmann, Bernd
The production of biogas is very complex process, which runs in some stages involving different microorganisms. Microbiological diversity of the process depends mainly on the composition of substrate and ambient conditions, such as process temperature. The fact is, the development and composition of the microbiological communities of the process are difficult to predict. Thus, the control and evaluation of such complex biological processes are very time consuming and expensive. In Germany the evaluation of the biogas plants can be performed according to the VDI-Norm 4630, which describes the methods for the evaluation of fermentation of organic materials including characterization of the substrate, sampling, collection of material data and fermentation tests. For that specially equipment and skilled personnel are required. Moreover, the evaluation procedure is very time consuming. That is why a new state-of-the-art alternative for the evaluation purposes is necessary to simplify and to speed up the assessment of the biogas production processes. The aim of this doctoral thesis is the development of a fast and reliable method for the evaluation of the biogas production processes. Therefore the mathematical modelling should identify significant process variables able to evaluate the whole process. For the optimization of mathematical models metaheuristic tools were used. In this doctoral thesis two different data sets were used – experimental data and simulated data. The experimental data were collected in projects “Biogas-Biocoenosis” (FKZ 22010711, Dr. Michael Klocke, Leibnitz-Institute für Agrartechnik und Bioökonomie e.V., Potsdam) and “Biogas-Enzyme” (FKZ 22027707, Dr. Monika Heiermann, Leibnitz-Institute für Agrartechnik und Bioökonomie e.V., Potsdam). The simulated data set was generated using the Anaerobic Digestion Model No.1 (ADM1). The chemical process variables were used as the independent process variable set, while the biogas production output represented the dependent process variable. Prediction of the biogas production was done using linear and nonlinear mathematic models. Here, Partial-Least-Square-Regression (PLSR), Locally-Weighted-Regression (LWR) and Artificial Neural Networks (ANN) were implemented. In order to identify the most significant undependable process variables optimization algorithms were used, Ant Colony Optimization (ACO) and Genetic Algorithm (GA). Prediction capacity was evaluated using two model evaluation variables, Root Mean Square Error (RMSE) and Coefficient of Determination (R2). Figure 1 in Supplementary represents the flow chart of the developed methodology applied for ADM1 generated data set. In Figure 2 (Supplementary) there is a flow chart of the developed methodology applied for the experimentally collected data. The developed approaches could be successfully used for the prediction of the desired process variable, biogas production rate. The variable selection done with the help of metaheuristic optimization algorithms improved the prediction results and reduced number of the independent process variables. Hydraulic retention time, dry matter, neutral detergent fibre, acid detergent fibre and n-butyric acid were identified as the most significant ones. The best prediction was obtained using ANN models. Here, the error of prediction was low and the coefficient of determination high. The successful implementation of the developed methodology proved mathematical models to be an effective alternative method capable to evaluate and to optimize complicated biological processes. Furthermore, it would be mandatory further experimental evaluation of the developed strategy, using the model-based process information.
Schätzung betrieblicher Kostenfunktionen mit künstlichen neuronalen Netzen
(2015) Simen, Jan-Philipp; Troßmann, Ernst
In this thesis a concept for estimating cost relationships with artificial neural networks is developed. The resulting open-source software application Cenobi (http://sourceforge.net/projects/cenobi/) is able to assess the impact of cost drivers on activity cost, plot non-linear cost functions, do forecasting and budgeting, calculate incremental cost, do unit costing, job costing etc., calculate cost driver rates and analyse cost variances. An object-oriented implementation of neural networks optimized by genetic algorithms provides the basis for these calculations.