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Abstract This work presents a novel study for identifying alterations in the control states of a desuperheater system based on real closed-loop data from a coal-fired power plant operating under various loads using linear and nonlinear system identification techniques. Specifically, Transfer Functions (TFs) and Gaussian Processes within a Nonlinear AutoRegressive eXogenous model (GP-NARX) are utilized. The desuperheater system comprises two units, north and south, each modeled as a single-input single-output (SISO) system based on spray valve positions and outlet temperatures. To identify changes in the control states using TFs, deviations in the coefficients of three poles and two zeros transfer functions are analyzed. Significant shifts in the control states of the north desuperheater are observed when transitioning from nominal to half and low loads, with deviations of up to four orders of magnitude. Substantial changes in control states are also observed for the south desuperheater when moving from nominal to low load, with a deviation in the coefficients of up to five orders of magnitude, whereas the transition from nominal to half load shows a smaller deviation of up to three orders of magnitude. In the GP-NARX approach, model uncertainties are used to indicate the changes in the control states. The south desuperheater showed a significant uncertainty of up to 8°F from the nominal to the low load, evidencing a change in the control states. Regarding the north desuperheater, increased uncertainty, up to 6°F, is also observed but in shorter time intervals when compared to the south desuperheater. Ultimately, this work shows that both approaches can be used as a basis for system identification, employing real closed-loop power plant data. 

Abstract Understanding the effects of climate-mediated environmental variation on the distribution of organisms is critically important in an era of global change. We used wavelet analysis to quantify the spatiotemporal (co)variation in daily water temperature for predicting the distribution of cryptic refugia across 16 intertidal sites that were characterized as ‘no’, ‘weak’ or ‘strong’ upwelling and spanned 2000 km of the European Atlantic Coast. Sites experiencing weak upwelling exhibited high synchrony in temperature but low levels of co-variability at monthly to weekly timescales, whereas the opposite was true for sites experiencing strong upwelling. This suggests upwelling generates temporal thermal refugia that can promote organismal performance by both supplying colder water that mitigates thermal stress during hot Summer months and ensuring high levels of fine-scale variation in temperature that reduce the duration of thermal extremes. Additionally, pairwise correlograms based on the Pearson-product moment correlation coefficient and wavelet coherence revealed scale dependent trends in temperature fluctuations across space, with a rapid decay in strong upwelling sites at monthly and weekly timescales. This suggests upwelling also generates spatial thermal refugia that can ‘rescue’ populations from unfavorable conditions at local and regional scales. Overall, this study highlights the importance of identifying cryptic spatiotemporal refugia that emerge from fine-scale environmental variation to map potential patterns of organismal performance in a rapidly changing world. 

Abstract The objective in this work is to propose a novel approach for solving inverse problems from the output space to the input space using automatic differentiation coupled with the implicit function theorem and a path integration scheme. A common way of solving inverse problems in process systems engineering (PSE) and in science, technology, engineering and mathematics (STEM) in general is using nonlinear programming (NLP) tools, which may become computationally expensive when both the underlying process model complexity and dimensionality increase. The proposed approach takes advantage of recent advances in robust automatic differentiation packages to calculate the input space region by integration of governing differential equations of a given process. Such calculations are performed based on an initial starting point from the output space and are capable of maintaining accuracy and reducing computational time when compared to using NLP‐based approaches to obtain the inverse mapping. Two nonlinear case studies, namely a continuous stirred tank reactor (CSTR) and a membrane reactor for conversion of natural gas to value‐added chemicals are addressed using the proposed approach and compared against: (i) extensive (brute‐force) search for forward mapping and (ii) using NLP solvers for obtaining the inverse mapping. The obtained results show that the novel approach is in agreement with the typical approaches, while computational time and complexity are considerably reduced, indicating that a new direction for solving inverse problems is developed in this work.