optimal modelbased experimental design in batch

optimal modelbased experimental design in batch


643b) Experimental and Model-Based Design of the

Within the experimental framework described above, a PHB accumulation of the order of 55-60% wt. of the cellular dry mass (CDM) was measured under batch operating conditions. A maximum value for the PHB concentration and intracellular content were observed for an initial carbon to nitrogen ratio (C/N) equal to 10 and a residence time at the

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PDF Experiment design for batch-to-batch model-based learning

Experiment design for batch-to-batch model-based learning control Marco Forgione 1, Xavier Bombois and Paul M.J. Van den Hof2 Abstract—An Experiment Design framework for dynamical systems which execute multiple batches is presented in this paper. After each batch, a model of the system dynamics is refined using the measured data.

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Experimental Setup and Controller Design for An Hcci

Two type of controller design approaches are used for designing HCCI controllers: (1) empirical, and (2) model-based. A discrete sub-optimal sliding mode controller (DSSMC) is designed as a model-based controller to control CA50 and T exh, and a PI controller is designed to control IMEP. The results show that the designed controllers can

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PDF Batch Distillation: Simulation and Experimental Validation

problems in batch distillation. Their work conclude that optimal reflux ratio is comparable with constant reflux ratio for batch distillation columns. In this work a mathematical model based on mass balances and the equilibrium vapor-liquid equations has been developed. The industry needs rapid models like this one presented in this work.

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Frontiers | Design Optimization of a Pneumatic Soft

We present two frameworks for design optimization of a multi-chamber pneumatic-driven soft actuator to optimize its mechanical performance. The design goal is to achieve maximal horizontal motion of the top surface of the actuator with a minimum effect on its vertical motion. The parametric shape and layout of air chambers are optimized individually with the firefly algorithm and a deep

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Sequential model-based A- and V-optimal design of

Sequential model-based V-optimal design was used by Thompson et al. (2010) to obtain accurate model prediction for the molecular-weight-distribution of ethylene copolymers produced using Ziegler-Natta-catalyzed (Thompson et al., 2010). In the current article, V-optimal design is considered for selecting experimental settings in a pharmaceutical

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Model-Based Optimization of a Fed-Batch Bioreactor for mAb

The model-based optimal operation of a bioreactor can be applied in two ways: (a) offline, in which an optimal operating policy is determined by using an adequate kinetic model of the previously identified process, and (b) online, using a simplified (often empirical) model and a classic state-parameter estimator, based on online recorded data

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Nonlinear Control of a Batch Polymerization Reactor: an

This article first describes the experimental system and de- velops its mathematical model. Then, the results of dynamic optimization of the batch reactor (the optimal loading and operating conditions) are presented, as well as the estimation of the process parameters of the experimental system. Subse-

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Validation of a Model for Biodiesel Production through

Advanced model-based experiment design techniques are a reliable tool for the rapid development and refining of process models. Through a practical case study of current interest (a biodiesel production process), we demonstrate the validity of this approach as applied to the planning of optimal experiments for complex kinetics elucidation. The need for an appropriate use of these tools, in

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Model-based DoE for feed batch cultivation of a CHO cell

The process was optimized using a model based design instead of performing various experiments in the laboratory. Based on a few shaking flask experiments for kinetic parameter determination,the model was tested for data generation on common fed-batch strategies. Optimized conditions were selected by means of DoE strategies and tested

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Fed-batch Saccharomyces cerevisiae fermentation of

The model is combined with a statistical experimental design to determine an optimal operating strategy that maximizes ethanol production and serves for the systematic evaluation of critical process variables. In particular, the effects of various operating conditions and feeding strategies on the dynamic behavior of batch and fed-batch

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Optimal experiment design | Technologies | gPROMS

gPROMS’ model-based experiment design capabilities lead to dramatic increases in the efficiency and effectiveness of laboratory experimentation. This translates into fewer, well designed, experiments resulting in more accurate estimates for model parameters, which in turn means lower design uncertainty and more accurate scale-up – overall, faster product and process development cycles.

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Model-Based Design and Process Optimization of Continuous

Model-Based Design and Process Optimization of model-based optimization allowing significant reduction of experimental e orts and applying the Quality by Design (QbD) approach consistently. (PAT) for pharmaceutical manufacturing [23-27]. Switching from batch to continuous processes is a promising possibility to overcome critical

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machine learning - How to calculate optimal batch size

Don't forget to linearly increase your learning rate when increasing the batch size. Let's assume we have a Tesla P100 at hand with 16 GB memory. (16000 - model_size) / (forward_back_ward_size) (16000 - 4.3) / 18.25 = 1148.29 rounded to powers of 2 results in batch size

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Microbial production of polyhydroxybutyrate with tailor

The microbial production of polyhydroxybutyrate (PHB) is a complex process in which the final quantity and quality of the PHB depend on a large number of process operating variables. Consequently, the design and optimal dynamic operation of a microbial process for the efficient production of PHB wit

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Design of Experiments | JMP

Design of Experiments (DOE) with JMP. Design of experiments, or DOE, is a practical and ubiquitous approach for exploring multifactor opportunity spaces, and JMP offers world-class capabilities for design and analysis in a form you can easily use. Methodical experimentation has many applications for efficient and effective information gathering.

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Model Identification and Control Strategies for Batch

The open-loop optimal control strategy to regulate the crystal-size distribution of use in experimental design are presented. Measurements of solute concentration in model- based strategy for batch cooling crystallizers that handles in- put, output, and final-time constraints. The interplay between

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Experimental design | Emukit

For instance, a D-optimal design aims to maximize the differential Shannon information content of the model parameter estimates; an I-optimal design seeks to minimize the average prediction variance over the entire design space. See for a general review on experimental design of these type with Bayesian modes.

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Optimal design and operation of batch reactors. 2. A case

Sequential Experimental Design Strategy for Optimal Batch Profiles Using Hybrid Function Approximations. Industrial & Engineering Chemistry Research 2004, 43 (17) , 5260-5274.

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Optimal Design of Experiments: A Case Study Approach: Goos

Next an optimal experimental design is constructed and the data with full detailed analysis provided. Statisticians and para-statisticians alike should enjoy this book. Clearly a new day is dawning in the art and practice of experimental design." ―J. Stuart Hunter, Professor Emeritus, Princeton University

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Optimal experimental design and artificial neural networks

This process involves a series of complex reactions. Therefore, an empirical model based on artificial neural networks has been developed for fitting the experimental data obtained in a laboratory batch reactor for the degradation of 2,4-dimethyl aniline (2,4-xylidine), chosen as a model pollutant.

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Using experimental design to optimize the process

A face-centered central composite design was applied in order to optimize the granulation process on a semi-full scale (30-kg batch) for the geometric mean granule size. The granulation process variables investigated were: inlet air temperature, inlet airflow rate, spray rate and inlet air humidity.

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NSF Award Search: Award # 1537987 - Collaborative Research

The Active Learning problem in Machine Learning is analogous to a classic problem in the field of Statistics, namely, optimal experimental design. Suppose one has to conduct a series of tests on an engineering process (e.g., some machine) in order to improve the process response (e.g., some measure of the quality or performance of a process).

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PDF MEETING ABSTRACT Open Access fed-batch processes model

MEETING ABSTRACT Open Access DoE of fed-batch processes - model-based design and experimental evaluation Onur Sercinoglu1, Oscar Platas Barradas1, Volker Sandig2, An-Ping Zeng1, Ralf Pörtner1* From 22nd European Society for Animal Cell Technology (ESACT) Meeting on Cell Based Technologies

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Four Quick Steps to Production: Using Model-Based Design

Storing large amounts of data for batch processing is not a realistic receiver design for an embedded system. The combination of a powerful RF front end like the AD9361 and a technical computing language like MATLAB ® greatly simplifies the problems associated with detecting and decoding these transmissions.

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