Application of Hybrid Genetic Algorithm Routine in Optimizing Food and Bioengineering Processes

Pelleting of Pine and Switchgrass Blends: Effect of Process Variables and Blend Ratio on the Pellet Quality and Energy Consumption

The blending of woody and herbaceous biomass can influence pellet quality and the energy consumption of the process. This work aims to understand the pelleting characteristics of 2-inch top-pine residue blended with switchgrass at high moisture content. The process variables tested are blend moisture content, length-to-diameter (L/D) ratio in the pellet die, and the blend ratio. A flat die pellet mill was also used in this study. The pine and switchgrass blend ratios that were tested include: (1) 25% 2-inch top pine residue with 75% switchgrass; (2) 50% 2-inch top pine residue with 50% switchgrass; and (3) 75% 2-inch top pine residue with 25% switchgrass. The pelleting process conditions tested included the L/D ratio in the pellet die (i.e., 1.5 to 2.6) and the blend moisture content (20 to 30%, w.b.). Analysis of experimental data indicated that blending 25% switchgrass with 75% 2-inch top pine residue and 50% switchgrass with 50% 2-inch top pine residue resulted in pellets with a bulk density of > 550 kg/m3 and durability of > 95%. Optimization of the response surface models developed for process conditions in terms of product properties indicated that a higher L/D ratio of 2.6 and a lower blend-moisture content of 20% (w.b.) maximized bulk density and durability. Higher pine in the blends improved the pellet durability and reduced energy consumption.

ACE: A Workbench Using Evolutionary Genetic Algorithms for Analyzing Association in TCGA

Modern methods of acquiring molecular data have improved rapidly in recent years, making it easier for researchers to collect large volumes of information. However, this has increased the challenge of recognizing interesting patterns within the data. Atlas Correlation Explorer (ACE) is a user-friendly workbench for seeking associations between attributes in The Cancer Genome Atlas (TCGA) database. It allows any combination of clinical and genomic data streams to be searched using an evolutionary algorithm approach. To showcase ACE, we assessed which RNA sequencing transcripts were associated with estrogen receptor (ESR1) in the TCGA breast cancer cohort. The analysis revealed already well-established associations with XBP1 and FOXA1, but also identified a strong association with CT62, a potential immunotherapeutic target with few previous associations with breast cancer. In conclusion, ACE can produce results for very large searches in a short time and will serve as an increasingly useful tool for biomarker discovery in the big data era. SIGNIFICANCE: ACE uses an evolutionary algorithm approach to perform large searches for associations between any combinations of data in the TCGA database.

Biomass Grinding Process Optimization Using Response Surface Methodology and a Hybrid Genetic Algorithm

Biomass could be a key source of renewable energy. Agricultural waste products, such as corn stover, provide a convenient means to replace fossil fuels, such as coal, and a large amount of feedstock is currently available for energy consumption in the U.S. This study has two main objectives: (1) to understand the impact of corn stover moisture content and grinder speed on grind physical properties; and (2) develop response surface models and optimize these models using a hybrid genetic algorithm. The response surface models developed were used to draw surface plots to understand the interaction effects of the corn stover grind moisture content and grinder speed on the grind physical properties and specific energy consumption. The surface plots indicated that a higher corn stover grind moisture content and grinder speed had a positive effect on the bulk and tapped density. The final grind moisture content was highly influenced by the initial moisture content of the corn stover grind. Optimization of the response surface models using the hybrid genetic algorithm indicated that moisture content in the range of 17 to 19% (w.b.) and a grinder speed of 47 to 49 Hz maximized the bulk and tapped density and minimized the geomantic mean particle length. The specific energy consumption was minimized when the grinder speed was about 20 Hz and the corn stover grind moisture content was about 10% (w.b.).

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Biomarker detection is one of the more important biomedical questions for high-throughput 'omics' researchers, and almost all existing biomarker detection algorithms generate one biomarker subset with the optimized performance measurement for a given dataset. However, a recent study demonstrated the existence of multiple biomarker subsets with similarly effective or even identical classification performances. This protocol presents a simple and straightforward methodology for detecting biomarker subsets with binary classification performances, better than a user-defined cutoff. The protocol consists of data preparation and loading, baseline information summarization, parameter tuning, biomarker screening, result visualization and interpretation, biomarker gene annotations, and result and visualization exportation at publication quality. The proposed biomarker screening strategy is intuitive and demonstrates a general rule for developing biomarker detection algorithms. A user-friendly graphical user interface (GUI) was developed using the programming language Python, allowing biomedical researchers to have direct access to their results. The source code and manual of kSolutionVis can be downloaded from http://www.healthinformaticslab.org/supp/resources.php.