Data-Driven Dynamic Modeling of Coupled Thermal and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

Data-Driven Dynamic Prediction of Interrelated Heat and Electric Outputs of Microturbines

Microturbines are among the most successfully commercialized distributed energy resources, especially when they are used for combined heat and power generation. However, the interrelated thermal and electrical system dynamic behaviors have not been fully investigated. This is technically challenging due to the complex thermo-fluid-mechanical energy conversion processes which introduce multiple time-scale dynamics and strong nonlinearity into the analysis. To tackle this problem, this paper proposes a simplified model which can predict the coupled thermal and electric output dynamics of microturbines. Considering the time-scale difference of various dynamic processes occuring within microturbines, the electromechanical subsystem is treated as a fast quasi-linear process while the thermo-mechanical subsystem is treated as a slow process with high nonlinearity. A three-stage subspace identification method is utilized to capture the dominant dynamics and predict the electric power output. For the thermo-mechanical process, a radial basis function model trained by the particle swarm optimization method is employed to handle the strong nonlinear characteristics. Experimental tests on a Capstone C30 microturbine show that the proposed modeling method can well capture the system dynamics and produce a good prediction of the coupled thermal and electric outputs in various operating modes.

A Generalized Fuzzy Linguistic Model for Predicting Component Concentrations in an Optical Gas Sensing System

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NOx, SO2 etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

A generalized fuzzy linguistic model for predicting component concentrations in an optical gas sensing system

Motivated by environmental protection concerns, monitoring the flue gas of thermal power plant is now often mandatory due to the need to ensure that emission levels stay within safe limits. Optical based gas sensing systems are increasingly employed for this purpose, with regression techniques used to relate gas optical absorption spectra to the concentrations of specific gas components of interest (NO_x, SO₂ etc.). Accurately predicting gas concentrations from absorption spectra remains a challenging problem due to the presence of nonlinearities in the relationships and the high-dimensional and correlated nature of the spectral data. This article proposes a generalized fuzzy linguistic model (GFLM) to address this challenge. The GFLM is made up of a series of “If-Then” fuzzy rules. The absorption spectra are input variables in the rule antecedent. The rule consequent is a general nonlinear polynomial function of the absorption spectra. Model parameters are estimated using least squares and gradient descent optimization algorithms. The performance of GFLM is compared with other traditional prediction models, such as partial least squares, support vector machines, multilayer perceptron neural networks and radial basis function networks, for two real flue gas spectral datasets: one from a coal-fired power plant and one from a gas-fired power plant. The experimental results show that the generalized fuzzy linguistic model has good predictive ability, and is competitive with alternative approaches, while having the added advantage of providing an interpretable model.

Transcallosal connectivity of the human cortical motor network

The organizational and architectural configuration of white matter pathways connecting brain regions has ramifications for all facets of the human condition, including manifestations of incipient neurodegeneration. Although diffusion tensor imaging (DTI) has been used extensively to visualize white matter connectivity, due to the widespread presence of crossing fibres, the lateral projections of the corpus callosum are not normally detected using this methodology. Detailed knowledge of the transcallosal connectivity of the human cortical motor network has therefore remained elusive. We employed constrained spherical deconvolution (CSD) tractography - an approach that is much less susceptible to the influence of crossing fibres, in order to derive complete in-vivo characterizations of white matter pathways connecting specific motor cortical regions to their counterparts and other loci in the opposite hemisphere. The revealed patterns of connectivity closely resemble those derived from anatomical tracing in primates. It was established that dorsal premotor cortex (PMd) and supplementary motor area (SMA) have extensive interhemispheric connectivity - exhibiting both dense homologous projections, and widespread structural relations with every other region in the contralateral motor network. Through this in-vivo portrayal, the importance of non-primary motor regions for interhemispheric communication is emphasized. Additionally, distinct connectivity profiles were detected for the anterior and posterior subdivisions of primary motor cortex. The present findings provide a comprehensive representation of transcallosal white matter projections in humans, and have the potential to inform the development of models and hypotheses relating structural and functional brain connectivity.

CTCF modulates Estrogen Receptor function through specific chromatin and nuclear matrix interactions

Enhancer regions and transcription start sites of estrogen-target regulated genes are connected by means of Estrogen Receptor long-range chromatin interactions. Yet, the complete molecular mechanisms controlling the transcriptional output of engaged enhancers and subsequent activation of coding genes remain elusive. Here, we report that CTCF binding to enhancer RNAs is enriched when breast cancer cells are stimulated with estrogen. CTCF binding to enhancer regions results in modulation of estrogen-induced gene transcription by preventing Estrogen Receptor chromatin binding and by hindering the formation of additional enhancer-promoter ER looping. Furthermore, the depletion of CTCF facilitates the expression of target genes associated with cell division and increases the rate of breast cancer cell proliferation. We have also uncovered a genomic network connecting loci enriched in cell cycle regulator genes to nuclear lamina that mediates the CTCF function. The nuclear lamina and chromatin interactions are regulated by estrogen-ER. We have observed that the chromatin loops formed when cells are treated with estrogen establish contacts with the nuclear lamina. Once there, the portion of CTCF associated with the nuclear lamina interacts with enhancer regions, limiting the formation of ER loops and the induction of genes present in the loop. Collectively, our results reveal an important, unanticipated interplay between CTCF and nuclear lamina to control the transcription of ER target genes, which has great implications in the rate of growth of breast cancer cells.

Cell cycle-coupled expansion of AR activity promotes cancer progression

The androgen receptor (AR) is required for prostate cancer (PCa) survival and progression, and ablation of AR activity is the first line of therapeutic intervention for disseminated disease. While initially effective, recurrent tumors ultimately arise for which there is no durable cure. Despite the dependence of PCa on AR activity throughout the course of disease, delineation of the AR-dependent transcriptional network that governs disease progression remains elusive, and the function of AR in mitotically active cells is not well understood. Analyzing AR activity as a function of cell cycle revealed an unexpected and highly expanded repertoire of AR-regulated gene networks in actively cycling cells. New AR functions segregated into two major clusters: those that are specific to cycling cells and retained throughout the mitotic cell cycle ('Cell Cycle Common'), versus those that were specifically enriched in a subset of cell cycle phases ('Phase Restricted'). Further analyses identified previously unrecognized AR functions in major pathways associated with clinical PCa progression. Illustrating the impact of these unmasked AR-driven pathways, dihydroceramide desaturase 1 was identified as an AR-regulated gene in mitotically active cells that promoted pro-metastatic phenotypes, and in advanced PCa proved to be highly associated with development of metastases, recurrence after therapeutic intervention and reduced overall survival. Taken together, these findings delineate AR function in mitotically active tumor cells, thus providing critical insight into the molecular basis by which AR promotes development of lethal PCa and nominate new avenues for therapeutic intervention.