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In this study, the design and practical implementation of a novel, scalable plug-flow pilot plant for hydrothermal liquefaction of organic feedstock is presented. The overall discussion comprises the system’s design, process modelling, and simulation, as well as results for an experimental validation of the proposed design with a focus on fluid dynamics and heat transfer. The design criteria take into account the scalability of the plug-flow processing system, optimized non-isothermal flow conditions of highly viscous liquids in a tubular system at harsh process conditions, specifically high pressure and medium temperatures, and overall maintenance suitability. A novel forced flow oscillation system as well as unique heat exchange design to reduce the energy consumption during system operation, maximize local flow mixing, and minimize plugging are proposed and experimentally tested. To achieve a better understanding and optimization of Hydrothermal Liquefaction (HTL) (and other) processing systems, a mathematical model of heat transfer coupled with non-isothermal fluid flow was also developed and implemented.

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This work focuses on the effects of variable amount of genomic information in the Bayesian estimation of unknown variance components associated with single‐step genomic prediction. We propose a quantitative criterion for the amount of genomic information included in the model and use it to study the relative effect of genomic data on efficiency of sampling from the posterior distribution of parameters of the single‐step model when conducting a Bayesian analysis with estimating unknown variances. The rate of change of estimated variances was dependent on the amount of genomic information involved in the analysis, but did not depend on the Gibbs updating schemes applied for sampling realizations of the posterior distribution. Simulation revealed a gradual deterioration of convergence rates for the locations parameters when new genomic data were gradually added into the analysis. In contrast, the convergence of variance components showed continuous improvement under the same conditions. The sampling efficiency increased proportionally to the amount of genomic information. In addition, an optimal amount of genomic information in variance–covariance matrix that guaranty the most (computationally) efficient analysis was found to correspond a proportion of animals genotyped ***0.8. The proposed criterion yield a characterization of expected performance of the Gibbs sampler if the analysis is subject to adjustment of the amount of genomic data and can be used to guide researchers on how large a proportion of animals should be genotyped in order to attain an efficient analysis.

This work represents a novel mechanistic approach to simulate and study genomic networks with accompanying regulatory interactions and complex mechanisms of quantitative trait formation. The approach implemented in MeSCoT software is conceptually based on the omnigenic genetic model of quantitative (complex) trait, and closely imitates the basic in vivo mechanisms of quantitative trait realization. The software provides a framework to study molecular mechanisms of gene-by-gene and gene-by-environment interactions underlying quantitative trait’s realization and allows detailed mechanistic studies of impact of genetic and phenotypic variance on gene regulation. MeSCoT performs a detailed simulation of genes’ regulatory interactions for variable genomic architectures and generates complete set of transcriptional and translational data together with simulated quantitative trait values. Such data provide opportunities to study, for example, verification of novel statistical methods aiming to integrate intermediate phenotypes together with final phenotype in quantitative genetic analyses or to investigate novel approaches for exploiting gene-by-gene and gene-by-environment interactions.