Optimization of red hue in wool dyeing using Mexican cochineal through sequential experimental designs

Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO2 capture process

In this paper, a methodology is developed for sequential design of experiments (SDoE) for process systems and applied to a solvent-based CO2 capture system. In this approach, the prior knowledge of the system is used to prioritize process data collection at specific operating conditions. These data are then incorporated into a Bayesian inference methodology for updating a stochastic model by refining estimations of its underlying parameters, and the updated model is then used to generate the next set of test runs. Thus, the new knowledge obtained from the data is used to guide subsequent iterations of the experimental runs, ensuring that the overall data collection is maximally informative given that most experimental campaigns, especially at pilot or higher-scale plants, are costly, time-consuming, and resource-limited. The test run objective for this work was to minimize the maximum model prediction uncertainty for key output variables, but the methodology is generic and can be readily applied to other test run objectives. This methodology is applied to an aqueous monoethanolamine (MEA) pilot plant campaign at the National Carbon Capture Center (NCCC) in Wilsonville, Alabama, USA. The SDoE framework was utilized for two iterations, while collecting 18 sets of data representing different process conditions, and this resulted in an overall average reduction in uncertainty of approximately 50% in the prediction of CO2 capture percentage. Moreover, 11 additional data sets were obtained with variation of absorber packing height for further model validation. This work shows the capability of the SDoE framework to maximize learning given limited resources, allowing for the reduction of model uncertainty, which is of great importance for many applications including reduction of technical risk associated with scale-up and economic analysis.

Sequential Design of Experiments to Maximize Learning from Carbon Capture Pilot Plant Testing

The use of natural colorants in modern dye factories is a potential green chemistry idea that should be widely promoted to minimize the dependency of wool dyeing on some hazardous and non-biodegradable synthetic colors. In this study, an effort was undertaken to see if Rheum emodi (rhubarb) extract might be used as a natural dye for wool dyeing for the replacement of synthetic dyes. The dyeing of wool fabric was carried out using microwave (MW) ray treatment. By combining several mordants, a stunning color palette of shades of varying hues and tones was created. Comparative evaluation of the effects of various chemical mordants (aluminum salt, iron salt, tannic acid and cream of tartar) and bio-mordants (pomegranate extract and pine nut hull extract) on the characteristics of dyed wool samples was carried out to choose the best mordant for each application. It was found that MW-treated wool fabric using a bio-mordant showed a higher colorfastness value of 4/5 to 5 and a color strength value of approximately 10–20 than using metallic mordant. Scanning electron microscopy photographs and Fourier transform infrared spectroscopy analyses revealed the difference between irradiated and unirradiated wool fabrics. The employment of MW rays and bio-mordants in the natural coloring of wool fabric is encouraged due to their biocompatibility and non-toxicity when combined with MW treatment of wool fabric, as well as their high colorfastness and color strength performance. As a result, naturally extracted dyes from rhubarb can be a replacement for synthetic dyes for the coloration of wool fabric in the textile industry due to their environmental friendliness.

This work, under the optimal experimental design framework, investigates the sensor placement problem that aims to guide electromagnetic induction (EMI) sensing of multiple objects. We use the linearized model covariance matrix as a measure of estimation error to present a sequential experimental design (SED) technique. The technique recursively minimizes data misfit to update model parameters and maximizes an information gain function for a future survey relative to previous surveys. The fundamental process of the SED seeks to increase weighted sensitivities to targets when placing sensors. The synthetic and field experiments demonstrate that SED can be used to guide the sensing process for an effective interrogation. It also can serve as a theoretic basis to improve empirical survey operation. We further study the sensitivity of the SED to the number of objects within the sensing range. The tests suggest that an appropriately overrepresented model about expected anomalies might be a feasible choice.

Surrogate-based sequential Bayesian experimental design using non-stationary Gaussian Processes

Conducting new experiments for biomass gasifiers is expensive and time consuming. Therefore, it is important to select conditions for new experiments so maximum information is obtained. In this study, sequential Bayesian model-based design of experiments (MBDoE) is used to design new experimental runs for a heat-integrated biomass downdraft gasifier. This MBDoE approach is valuable because it accounts for model structure, prior information about plausible parameter values, and previous experimental data in a relatively simple way. Operating conditions selected for each new run are biomass moisture content, water injection rate, and the desired energy demand from the downstream engine. Three types of MBDoE with different objective functions are considered: A-optimal, V-optimal, and a proposed new type of focused V-optimal design. A-optimal design is used to when the goal is to obtain improved parameter estimates, without specifying how the model will be used. Performing two new A-optimal runs reduced the standard deviations for model parameters by 18.4% on average compared to when only old data are available. The three most-improved parameter estimates are activation energies for char gasification reactions involving carbon dioxide, hydrogen, and steam, respectively. The focused V-optimal methodology results in greater improvements in prediction accuracy for tar concentration and outlet temperature, which are key model responses. Using two designed Vf-optimal runs reduces standard deviations for these variables by 59.4%, on average, compared to when only old data are available. New A-optimal and V-optimal runs lead to corresponding improvements of 30.7% and 50%, respectively.

This study describes the use of an analytical model, constructed using sequential design of experiments (DOEs), to optimize and quantify the uncertainty of a Diesel engine operating point. A genetic algorithm (GA) was also used to optimize the design. Three engine parameters were varied around a baseline design to minimize indicated specific fuel consumption (ISFC) without exceeding emissions (NOx and soot) or peak cylinder pressure constraints. An objective merit function was constructed to quantify the strength of designs. The engine parameters were start of injection (SOI), injection duration, and injector included angle. The engine simulation was completed with a sector mesh in the commercial computational fluid dynamics (CFD) software CONVERGE, which predicted the combustion and emissions using a detailed chemistry solver with a reduced mechanism for n-heptane. The analytical model was constructed using the SmartUQ software using DOE responses to construct kernel emulators of the system. Each emulator was used to direct the placement of the next set of DOE points such that they improve the accuracy of the subsequently generated emulator. This refinement was either across the entire design space or a reduced design space that was likely to contain the optimal design point. After sufficient emulator accuracy was achieved, the optimal design point was predicted. A total of 5 sequential DOEs were completed, for a total of 232 simulations. A reduced design region was predicted after the second DOE that reduced the volume of the design space by 96.8%. The final predicted optimum was found to exist in this reduced design region. The sequential DOE optimization was compared to an optimization performed using a GA. The GA was completed using a population of 9 and was run for 71 generations. This study highlighted the strengths of both methods for optimization. The GA (known to be an efficient and effective method) found a better optimum, while the DOE method found a good optimum with fewer total simulations. The DOE method also ran more simulations concurrently, which is an advantage when sufficient computing resources are available. In the second part of the study, the analytical model developed in the first part was used to assess the sensitivity and robustness of the design. A sensitivity analysis of the design space around the predicted optimum showed that injection duration had the strongest effect on predicted results, while the included angle had the weakest. The uncertainty propagation was studied over the reduced design region found with the sequential DoE in the first part. The uncertainty propagation results demonstrated that for the relatively large variations in the input parameters, the expected variation in the ISFC and NOx results were significant. Finally, the predictions from the analytical model were validated against CFD results for sweeps of the input parameters. The predictions of the analytical model were found to agree well with the results from the CFD simulation.

마이크로 유전알고리즘은 적은 수의 개체 사용 및 무작위 개체 구성을 통한 돌연변이 기능 대체의 특징을 갖는 진화연산을 수행하여 일반적인 유전알고리즘이 갖는 각 세대당 많은 계산 량이 요구되는 단점을 극복하고자 하였다. 이러한 마이크로 알고리즘은 특히 설계변수가 3~5 개를 갖는 문제에 효율적이라는 것이 많은 연구자들에 의하여 알려졌다. 따라서 본 연구의 목적은 순차적 실험계획법과 마이크로 유전알고리즘을 이용한 최적화 알고리즘을 개발하는 것이며, 이를 수학예제와 구조물 문제에 적용하여 실용성을 확인하고자 한다. 순차적 실험계획법은 저자들의 선행연구에서 제안되었으며, 실험계획법과 반응표면법을 이용하는 근사최적화 기법에 의한 시행착오적인 반복과정을 최소화하고자 하는 방법으로써, 행렬실험과 평균분석을 반복 적용하는 개념이다.

Exploiting the information content offered by geoelectric data in an efficient manner requires careful selection of the electrode configurations to be used. This can be achieved using sequential experimental design techniques proposed over the past few years. However, these techniques become impractical when large-scale 2D or 3D experiments have to be designed. Even if sequential experimental design were applicable, acquisition of the resulting data sets would require an unreasonably large effort using traditional multielectrode arrays. We present a new, fully parallelized pole-bipole measuring strategy by which large amounts of data can be acquired swiftly. Furthermore, we introduce a new experimental design concept that is based on “complete” data sets in terms of linear independence. Complete data sets include a relatively small number of basis electrode configurations, from which any other configuration can be reconstructedby superposition. The totality of possible configurations is referred to as the comprehensive data set. We demonstrate the benefits of such reconstructions using eigenvalue analyses for the case of noise-free data. In the presence of realistic noise, such reconstructions lead to unstable results when only four-point (bipole-bipole) configurations are considered. In contrast, complete three-point (pole-bipole) data sets allow more stable reconstructions. Moreover, complete pole-bipole data sets can be acquired very efficiently with a fully parallelized system. Resolution properties of complete pole-bipole data sets are illustrated using both noise-free and noisy synthetic data sets. We also show results from a field survey performed over a buried waste disposal site, which further demonstrates the usefulness of our approach. Although this paper is restricted to 2D examples, it is trivial to extend the concept to 3D surveys, where the advantages of parallelized pole-bipole data acquisition become very significant.

Multivariate optimization of low-temperature cleanup followed by dispersive solid-phase extraction for detection of Bisphenol A and benzophenones in infant formula

A method of model discrimination and parameter estimation in enzyme kinetics is proposed. The experimental design and analysis of the model are carried out simultaneously and the stopping rule for experimentation is deduced by the experimenter when the probabilities a posteriori indicate that one model is clearly superior to the rest. A FORTRAN77 program specifically developed for joint designs is given. The method is very powerful, as indicated by its usefulness in the discrimination between models. For example, it has been successfully applied to three cases of enzyme kinetics (a single-substrate Michaelian reaction with product inhibition, a single-substrate complex reaction and a two-substrate reaction). By using this method the most probable model and the estimates of the parameters can be obtained in one experimental session. The FORTRAN77 program is deposited as Supplementary Publication SUP 50134 (19 pages) at the British Library (Lending Division), Boston Spa, Wetherby, West Yorkshire LS23 7BQ, U.K., from whom copies can be obtained on the terms indicated in Biochem. J. (1986) 233, 5.

Multilevel Monte Carlo (MLMC) methods have been used by the information-based complexity community in order to improve the computational efficiency of parametric integration. We extend this approach by relaxing the assumptions on differentiability of the simulation output. Relaxing the assumption on the differentiability of the simulation output makes the MLMC method more widely applicable to stochastic simulation metamodeling problems in industrial engineering. The proposed scheme uses a sequential experiment design which allocates effort unevenly among design points in order to increase its efficiency. The procedure's efficiency is tested on an example of option pricing in the Black-Scholes model.

Coupling liquid membrane and flow-injection technique as an analytical strategy for copper analysis in saline water

On the costs of parameter uncertainties. Effects of parameter uncertainties during optimization and design of experiments