Process Model Output Research Articles

Synergistic controls of water column stability and groundwater phosphate on coastal algal blooms

Algal blooms have been identified as one major threat to coastal safety and marine ecosystem functioning, but the dominant mechanism regulating the formation of algal blooms remains controversial, ranging from physical control (via water column stability), the chemical control (via coastal nutrients) to joint control. Here we leveraged the unique data collected in the Hong Kong water over the annual cycle and past three decades, including direct observations of algal blooms and coastal nutrients and process model output of water column stability, and evaluated the differential competing hypotheses in regulating algal blooms. Our results demonstrate that the joint mechanism rather than the single mechanism effectively predicts all algal blooms. Meanwhile, we observed that the adequate nutrients (phosphate, PO43−) significantly originate from coastal groundwater. The production and fluctuation of PO43− in beach aquifers are primarily governed by groundwater temperature, leading to a sustained and sufficient supply of PO43− in a low groundwater temperature environment. Furthermore, along with submarine groundwater discharge (SGD), the ongoing release of PO43− in groundwater enters coastal waters and serves as sufficient nourishment for promoting algal blooms in coastal areas. These results highlight the importance of both physical and chemical mechanisms, as well as SGD, in regulating coastal algal blooms. These findings have practical implications for the prevention of coastal algal blooms and provide insights into mariculture, water security, and the sustainability of coastal ecosystems.

Projection of future drought and extreme events occurrence in Goodwater Creek Experimental Watershed, Midwestern US

ABSTRACT Predicting the impacts of projected change in precipitation (P) and temperature (T) on occurrence of drought and extreme events is essential for managing natural resources and setting policy. This study compares future occurrence of excessively dry and wet periods based on P, T, stream flow, soil moisture, and extreme P and T events. The comparisons are based on coupled future climate projections from multiple Earth system models downscaled using site-specific weather data and hydrologic model outputs for the Goodwater Creek Experimental Watershed, Missouri, USA. The use of multiple drought indices, downscaled climate data, and process model output facilitated drought prediction for different land surface processes and its comparison. The P and T extremes and droughts were calculated using standardized indices. The results based on drought and extreme indices indicate increased frequency and duration of drought in the future, primarily due to a projected decline in summer precipitation resulting in summer droughts. The streamflow and soil water-based drought indices indicated increased spring drought risks in the future despite a precipitation increase, indicating the importance of process representation with hydrologic models for drought computation.

Objective identification of thunderstorm gust fronts in numerical weather prediction models for fire weather forecasting

Abrupt changes in wind direction and speed can dramatically impact wildfire development and spread, endangering firefighters. A frequent cause of such wind shifts is outflow from thunderstorms and organised convective systems; thus, their identification and prediction present critical challenges for fire weather forecasters. Here, we develop a methodology and implement it in a software tool that can identify and depict convective outflow boundaries in high-resolution numerical weather prediction (NWP) models to provide guidance for fire weather forecasting. The tool can process model output, objectively identify gust fronts, and graphically display detected gust fronts and similar boundaries in NWP model forecasts. The tool is demonstrated with output from the Weather Research and Forecasting (WRF) model from the operational High-Resolution Rapid Refresh (HRRR) forecasting system and from a WRF ensemble run at the National Center for Atmospheric Research that can provide probabilistic information about model-predicted gust fronts. The tool can identify outflow boundaries in model forecasts of convective events occurring in both simple and complex terrain, both with and without concurrent wildfire activity. With accurate underlying model forecast output, the tool can reliably reveal areas of potential gust front activity and thus provide valuable guidance to incident meteorologists and command personnel.

New process optimization framework for laser assisted tape winding of composite pressure vessels: Controlling the unsteady bonding temperature

This paper presents an effective process optimization methodology for laser assisted tape winding (LATW) of complex part geometries by means of a numerical optical-thermal model. A winding path on the cylindrical and ellipsoidal (dome) part of a pressure vessel is considered with varying tooling curvature. First, the process model output is verified with the literature data based on the laser intensity distribution. Then, the transient laser irradiation and temperature distributions on the tape and substrate are described comprehensively. It is shown that the maximum laser intensity increases approximately by 80% and the process (bonding) temperature changes by 80 °C at the intersection of the cylindrical and dome section of the pressure vessel. In order to keep the transient process temperature constant, a robust optimization scheme is utilized by means of a genetic algorithm. The design variable is determined as the total laser power and temperature constraints are defined. The proposed optimization methodology regulates the temperature within 1.5 °C variation with respect to the desired value. In order to compensate the transient local curvature effects on the process temperature, the total laser power varies approximately between 30% and 175% of the reference (non-optimized) case.

Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations

Abstract. Natural wetlands constitute the largest and most uncertain source of methane (CH4) to the atmosphere and a large fraction of them are found in the northern latitudes. These emissions are typically estimated using process (“bottom-up”) or inversion (“top-down”) models. However, estimates from these two types of models are not independent of each other since the top-down estimates usually rely on the a priori estimation of these emissions obtained with process models. Hence, independent spatially explicit validation data are needed. Here we utilize a random forest (RF) machine-learning technique to upscale CH4 eddy covariance flux measurements from 25 sites to estimate CH4 wetland emissions from the northern latitudes (north of 45∘ N). Eddy covariance data from 2005 to 2016 are used for model development. The model is then used to predict emissions during 2013 and 2014. The predictive performance of the RF model is evaluated using a leave-one-site-out cross-validation scheme. The performance (Nash–Sutcliffe model efficiency =0.47) is comparable to previous studies upscaling net ecosystem exchange of carbon dioxide and studies comparing process model output against site-level CH4 emission data. The global distribution of wetlands is one major source of uncertainty for upscaling CH4. Thus, three wetland distribution maps are utilized in the upscaling. Depending on the wetland distribution map, the annual emissions for the northern wetlands yield 32 (22.3–41.2, 95 % confidence interval calculated from a RF model ensemble), 31 (21.4–39.9) or 38 (25.9–49.5) Tg(CH4) yr−1. To further evaluate the uncertainties of the upscaled CH4 flux data products we also compared them against output from two process models (LPX-Bern and WetCHARTs), and methodological issues related to CH4 flux upscaling are discussed. The monthly upscaled CH4 flux data products are available at https://doi.org/10.5281/zenodo.2560163 (Peltola et al., 2019).

The Monte Carlo driven and machine learning enhanced process simulator

This study presents a methodology with tools integration to apply advanced uncertainty propagation and sensitivity analysis in connection with commercial process simulation software. The methodology was applied to two processes: a heat pump system and a molecular distillation process. The input parameters of the selected thermodynamic model, namely critical temperature, critical pressure and acentric factor, were considered as a source of uncertainty and analysed using Monte Carlo sampling techniques. This enabled the process model output uncertainty to be described as an empirical distribution function with a 95% confidence interval. Variance-based decomposition such as the Sobol method or standard regression were used to analyse the sensitivity of the respective properties. We also show that machine learning methods such as polynomial chaos expansion (PCE) can be applied to reduce the number of necessary process simulations and obtained equivalent results in comparison with the more costly full Monte Carlo based procedure.

Uncertainty assessment of equations of state with application to an organic Rankine cycle

ABSTRACTEvaluations of equations of state (EoS) should include uncertainty. This study presents a generic method to analyse EoS from a detailed uncertainty analysis of the mathematical form and the data used to obtain EoS parameter values. The method is illustrated by comparison of Soave–Redlich–Kwong (SRK) cubic EoS with perturbed-chain statistical associating fluid theory (PC-SAFT) EoS for an organic Rankine cycle (ORC) for heat recovery to power from the exhaust gas of a marine diesel engine using cyclopentane as working fluid. Uncertainties of the EoS input parameters including their corresponding correlation structure, are quantified from experimental measurements using a bootstrap method. Variance-based sensitivity analysis is used to compare the uncertainties from the departure function and the ideal-gas contribution. A Monte Carlo procedure propagates fluid parameter input uncertainty onto the model outputs. Uncertainties in the departure function (SRK or PC-SAFT EoS) dominate the total uncertainties of the ORC model output. For this application and working fluid, SRK EoS has less predictive uncertainty in the process model output than does PC-SAFT EoS, though it cannot be determined if this is due to differences in the data for parameter estimation or in the mathematical form of the EoS or both.

Entropy Criteria in the Econometric Model Identification

A minmax problem statement of a problem of the parameter identification of econometric models is considered, based on the entropy (in the Shannon sense) of the identification error. An interconnection of the minmax criterion proposed and a criterion of the maximum of the mutual information (in the sense of the Kullback-Leibler divergence) of the output data generating process and econometric model output process is shown. Simulation results confirming the validity of the entropy-based approach proposed are presented.

Climate and weather influences on spatial temporal patterns of mountain pine beetle populations in Washington and Oregon

Widespread outbreaks of mountain pine beetle in North America have drawn the attention of scientists, forest managers, and the public. There is strong evidence that climate change has contributed to the extent and severity of recent outbreaks. Scientists are interested in quantifying relationships between bark beetle population dynamics and trends in climate. Process models that simulate climate suitability for mountain pine beetle outbreaks have advanced our understanding of beetle population dynamics; however, there are few studies that have assessed their accuracy across multiple outbreaks or at larger spatial scales. This study used the observed number of trees killed by mountain pine beetles per square kilometer in Oregon and Washington, USA, over the past three decades to quantify and assess the influence of climate and weather variables on beetle activity over longer time periods and larger scales than previously studied. Influences of temperature and precipitation in addition to process model output variables were assessed at annual and climatological time scales. The statistical analysis showed that new attacks are more likely to occur at locations with climatological mean August temperatures >15 degrees C. After controlling for beetle pressure, the variables with the largest effect on the odds of an outbreak exceeding a certain size were minimum winter temperature (positive relationship) and drought conditions in current and previous years. Precipitation levels in the year prior to the outbreak had a positive effect, possibly an indication of the influence of this driver on brood size. Two-year cumulative precipitation had a negative effect, a possible indication of the influence of drought on tree stress. Among the process model variables, cold tolerance was the strongest indicator of an outbreak increasing to epidemic size. A weather suitability index developed from the regression analysis indicated a 2.5x increase in the odds of outbreak at locations with highly suitable weather vs. locations with low suitability. The models were useful for estimating expected amounts of damage (total area with outbreaks) and for quantifying the contribution of climate to total damage. Overall, the results confirm the importance of climate and weather on the spatial expansion of bark beetle outbreaks over time.

Estimation and coordinated control for distributed parameter processes with a moving radiant actuator

This paper presents an estimation and control scheme for a class of industrial processes described by distributed parameter models with a moving radiant actuator. To overcome the lack of direct sensing alternatives for process state, a dual extended Kalman filter is established for estimating process status online based on a reduced process model and available output measurements. A distinct challenge that is addressed is the selection and construction of a suitable feedback signal from the distributed state estimate following the moving radiant actuator. The estimated status is then integrated into a rule-based feedback controller, which coordinates two manipulated variables of the moving radiant actuator to achieve the control objective. The two manipulated variables are the velocity and the radiant flux or power of the moving actuator. Both the estimation and feedback control strategies are demonstrated using computer simulations of one-dimensional distributed parameter models for an ultraviolet (UV) coating curing process involving a moving UV source. The results show that the proposed estimation and control schemes can significantly improve process quality and compensate for unknown disturbances on the target.

If approximating nonlinear areas, then consider fuzzy systems

The fuzzy system is a knowledge-based system consisting of linguistic if-then rules. This article will focus on the Takagi-Sugeno (TS) type models. A novel approach of INFUMO has been applied in fault detection. The INFUMO was derived using the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">infin</sub> -norm function approximation. The majority of papers focus on the construction of the so-called residual generator, a comparator of the process and the process-model output that creates a residual signal

Algorithm for Three-Step Estimation of Transfer Function with Unknown Delay Steps and Order

This paper deals with the estimation of an unknown process transfer function in the presence of colored measurement noise. A three-step estimation procedure has been previously developed for transfer functions, the delay steps and the orders of which are known in advance. The procedure is extended to deal with transfer functions with unknown delay steps and orders. The auto-correlation function of the error between the process output and model output is utilized for evaluating the model fitness. The effectiveness of the proposed method is demonstrated by a simulation study using a sample set of data in MATLAB.

Compensation of Dominant and Variable Delay in Process Systems

A technique is proposed for compensating dominant and variable delay in process systems. The basic structure of the Smith predictor is adopted, while the delay parameter in the process model is continuously tuned on-line by minimizing a performance index function. The average magnitude difference function, the expectation of the absolute difference between process output and process model output, is shown to be a good performance index function. The effectiveness of the proposed method is demonstrated through applications to two typical processes.

The replacement cost integration program: An engineering-economic model of oil supply

The Replacement Cost Integration Program (RCIP) forecasts the annual additions to proven reserves and production of domestic crude oil. The inputs to RCIP include parameters (discount rate, tax rate, and supply elasticity), expected oil prices, and process model output. The process model output consists of capital costs, operating costs, and production levels for 31 regions. For each region and year, RCIP calculates expected profits and forecasts the level of reserve additions. Two production profiles relate reserve additions to future oil production. The parameters in the model were estimated using data for 1970 to 1986. Results are presented for two sets of prices: high and low. For the period from 1987 to 2020, the cumulative production of oil increases by 64 percent from the low case to the high case, while the cumulative additions to proven reserves increase by more than a factor of three.

Feedback control of chemical processes using on-line optimization techniques

The objective of this research is to develop tools for feedback control and sensitivity analysis of systems modelled by nonlinear differential-algebraic equations. Features of this novel approach include: direct use of nonlinear models without linearization, the ability to handle multiple inputs and outputs without pairing, and the ability to handle input and output constraints without complicated anti-reset windup logic. In the model-predictive control framework, an optimal control policy for the nominal plant is determined using a simultaneous optimization and model solution approach. The sensitivity of the optimal solution with respect to the model parameters is also computed. The feedback from the measurements is used to update the important process model parameters and output disturbances. The optimal profile is then recomputed for the updated model. Confidence intervals can also be placed on the optimal control profiles by considering second-order variations in the Lagrangian. This approach will be illustrated with several examples including: batch and continuous chemical reactors, and a batch crystallizer, which demonstrates the method on a nonlinear, distributed parameter system.