Uncertain Components Research Articles

Integrating geometallurgy in the simulation of processing plant responses and applying policy gradient for shovel allocation in operating mining complexes

ABSTRACT Several short-term planning activities of mining complexes must be addressed simultaneously, such as extraction sequence, equipment allocation, digline definition, and destination decisions. Regarding stochastic mine planning, geological attributes and equipment performance uncertainties are more commonly addressed. However, processing response attributes at the ore treatment facilities also have uncertain components. This paper proposes a high-order simulation of processing plant responses given the blended ore attributes. First, it is included in the grade-control digline approach and later applied to a policy gradient-based framework that allocates shovels. A case study at a gold mining complex illustrates the benefits of the proposed approach.

Метод R-функцій для розв’язання задачі масообміну циліндричного тіла з твірною у вигляді кривої Ламе з в’язкою нестисливою рідиною

The problem of stationary mass exchange of a cylindrical body with viscous incompressible fluid flow is reduced to solving the equation for concentration with the corresponding boundary conditions on the surface of the body and at infinity. Applying the constructive apparatus of the R-functions theory allows us to accurately take into account the geometry of the domain, as well as the boundary conditions together with the condition at infinity. The R-function method was proposed by V. L. Rvachev, Ukraine National Academy of Sciences academician. For boundary value problems of mathematical physics, this method allows constructing the so-called structure of the solution of the boundary value problem, i.e. a bundle of functions that accurately takes into account all boundary conditions of the problem and depends on some uncertain components. The choice of these uncertain components is performed in such a way as to satisfy (in a certain sense) the equation of the problem. For this, one can use standard numerical methods of mathematical physics (the Ritz method, the Galerkin method, the least squares method, collocations method, etc.). It should be noted that the geometry of the region is taken into account exactly, i.e. in particular, there is no replacement of curvilinear sections of the boundary with polygonal lines inscribed in them, as it is done, for example, in the finite element method. The purpose of this article is to apply the R-functions method to the problem of mass exchange of a cylindrical body formed by the Lame curve, with a viscous incompressible fluid flowing around it. To achieve this goal, a complete structure of the solution of a linear boundary value problem for concentration is constructed by the R-functions theory methods, and a numerical algorithm based on the Galerkin method for approximating indefinite components in the problem structure is used. The degree of rigor and the conditions for using the considered equations of fluid motion are not considered in the work; these equations are considered as mathematical models subject to numerical algorithmization

Novel Recuperated Power Cycles for Cost-Effective Integration of Variable Renewable Energy

The ongoing transition to energy systems with high shares of variable renewables motivates the development of novel thermal power cycles that operate economically at low capacity factors to accommodate wind and solar intermittency. This study presents two recuperated power cycles with low capital costs for this market segment: (1) the near-isothermal hydrogen turbine (NIHT) concept, capable of achieving combined cycle efficiencies without a bottoming cycle through fuel combustion in the expansion path, and (2) the intercooled recuperated water-injected (IRWI) power cycle that employs conventional combustion technology at an efficiency cost of only 4% points. The economic assessment carried out in this work reveals that the proposed cycles increasingly outperform combined cycle benchmarks with and without CO2 capture as the plant capacity factor reduces below 50%. When the cost of fuel storage and delivery by pipelines is included in the evaluation, however, plants fired by hydrogen lose competitiveness relative to natural gas-fired plants due to the high fuel delivery costs caused by the low volumetric energy density of hydrogen. This important but uncertain cost component could erode the business case for future hydrogen-fired power plants, in which case the IRWI concept powered by natural gas emerges as a promising solution.

Global soil respiration predictions with associated uncertainties from different spatio-temporal data subsets

Soil respiration (Rs), the second-largest flux in the global carbon cycle, is a crucial but uncertain component. To improve the understanding of global Rs, we constructed single global models, and specific models classified by climate type, land cover type, year of the data record, and elevation range using the random forest algorithm to predict global Rs values and explore the associated uncertainty in the models. The results showed a similar overall predictive performance for the models, with an R-squared value greater than 0.63; however, significant differences were observed compared to the global Rs estimate (23 Pg C). All the models estimated larger values of Rs than the single global model, mainly owing to imbalances in the sample data on which the prediction models were based. One exception to this result is the land cover model, which estimates a smaller global Rs for 2020 (95.1 Pg C). Overall, the single global model estimates were closer to those obtained for temperate zones owing to differences in the training data distribution, which resulted in smaller global estimates than those of other classification-specific models. Prediction models using observations before 2000 tend to underestimate the global Rs. However, the use of classification-specific Rs models proved helpful in addressing the persistent temporal and spatial imbalances in Rs sampling. Expanding the coverage of Rs records both temporally and spatially and updating the global Rs database promptly would improve the estimation accuracy of global Rs prediction models while enhancing the understanding of the overall global carbon budget and the feedback of soil carbon with regard to climate warming.

Water budget-based evapotranspiration product captures natural and human-caused variability

Abstract Evapotranspiration (ET) is one of the most important yet highly uncertain components of the water cycle. Available modelled ET products do not necessarily agree with each other at various spatiotemporal scales, either due to limitations on input data and/or due to model assumptions and simplifications. Therefore, using water budget equation to estimate ET has gained attention. However, a large number of water-budget combinations with large uncertainties are available that increases ambiguity in choosing the best ET estimate. Here, the Kalman filter is employed for ingesting 96 water-budget based ET estimates and produce a global ET product with uncertainty < 2 mm, and it captures the general spatiotemporal pattern of ET and the inter-annual variability over all continents. Since the water budget includes storage change due to human interventions, our ET estimates are superior over regions with strong irrigation signal, such as the Ganges basin. We verify our claim by using a modified variable infiltration capacity model that simulates irrigation activities as well. Our ET estimates have a global mean positive trend of 0.18 ± 0.02 mm/year with larger regional variations that are discussed.

Improving Direct Yaw-Moment Control via Neural-Network-Based Non-Singular Fast Terminal Sliding Mode Control for Electric Vehicles.

Given the increased significance of electric vehicles in recent years, this study aimed to develop a novel form of direct yaw-moment control (DYC) to enhance the driving stability of four-wheel independent drive (4WID) electric vehicles. Specifically, this study developed an innovative non-singular fast terminal sliding mode control (NFTSMC) method that integrates NFTSM and a fast-reaching control law. Moreover, this study employed a radial basis function neural network (RBFNN) to approximate both the entire system model and uncertain components, thereby reducing the computational load associated with a complex system model and augmenting the overall control performance. Using the aforementioned factors, the optimal additional yaw moment to ensure the lateral stability of a vehicle is determined. To generate the additional yaw moment, we introduce a real-time optimal torque distribution method based on the vertical load ratio. The stability of the proposed approach is comprehensively verified using the Lyapunov theory. Lastly, the validity of the proposed DYC system is confirmed by simulation tests involving step and sinusoidal inputs conducted using Matlab/Simulink and CarSim software. Compared to conventional sliding mode control (SMC) and NFTSMC methods, the proposed approach showed improvements in yaw rate tracking accuracy for all scenarios, along with a significant reduction in the chattering phenomenon in control torques.

Trajectory Tracking Control of Mobile Manipulator Based on Improved Sliding Mode Control Algorithm

Research on trajectory tracking control for climbing welding robots holds significant importance in the field of automated welding. However, existing trajectory tracking methods suffer from issues such as jitter and slow speed. In this paper, an improved sliding mode control strategy is proposed based on the self-designed wall-climbing welding mobile manipulator. Firstly, a new adaptive sliding mode control strategy is proposed for the mobile platform based on the kinematic model. By introducing a new approach law, the controller is designed when the distance between the center of mass is unknown. Secondly, regarding the manipulator, we analyze simplified dynamic equations, extract uncertain components, and utilize a CNN for compensation. This compensation strategy is integrated into the sliding mode control law, achieving precise control over the manipulator and effectively resolving issues like slow tracking speeds, large errors, and chattering. The stability of the robot control system is proved by the Lyapunov function. Through simulation analysis and experimental validation, the proposed control method is confirmed to be feasible and superior.

The Gas-phase Mass–Metallicity Relation for Massive Galaxies at z ∼ 0.7 with the LEGA-C Survey

The massive end of the gas-phase mass–metallicity relation (MZR) is a sensitive probe of active galactic nuclei (AGN) feedback that is a crucial but highly uncertain component of galaxy evolution models. In this paper, we extend the z ∼ 0.7 MZR by ∼0.5 dex up to log (M ⋆/M ⊙) ∼ 11.1. We use extremely deep VLT VIMOS spectra from the Large Early Galaxy Astrophysics Census (LEGA-C) survey to measure metallicities for 145 galaxies. The LEGA-C MZR matches the normalization of the z ∼ 0.8 DEEP2 MZR where they overlap, so we combine the two to create an MZR spanning from 9.3 to 11.1 log (M ⋆/M ⊙). The LEGA-C+DEEP2 MZR at z ∼ 0.7 is offset to slightly lower metallicities (0.05–0.13 dex) than the z ∼ 0 MZR, but it otherwise mirrors the established power-law rise at low/intermediate stellar masses and asymptotic flattening at high stellar masses. We compare the LEGA-C+DEEP2 MZR to the MZR from two cosmological simulations (IllustrisTNG and SIMBA), which predict qualitatively different metallicity trends for high-mass galaxies. This comparison highlights that our extended MZR provides a crucial observational constraint for galaxy evolution models in a mass regime where the MZR is very sensitive to choices about the implementation of AGN feedback.

Improving the Quality of Industrial Robot Control Using an Iterative Learning Method with Online Optimal Learning and Intelligent Online Learning Function Parameters

It is inevitable that the characteristics of a robot system change inaccurately or cannot be accurately determined during movement and are affected by external disturbances. There are many adaptive control methods, such as the exact linearization method, sliding control, or neural control, to improve the quality of trajectory tracking for a robot’s motion system. However, those methods require a great deal of computation to solve the constrained nonlinear optimization problem. This article first presents some techniques for determining the online learning function parameters of an intelligent controller, including two circuits: the inner circuit is an uncertain function component estimator to compensate for the robot’s input, and the outer circuit is an iterative learning controller and does not use a mathematical model of the robot with optimal online learning function parameters. The optimal condition is based on the model in the time domain to determine the learning function parameters that change adaptively according to the sum of squared tracking errors of each loop. As for the intelligent online learning function parameters, they closely follow the general model to stabilize the robot system, based on the principle of intelligent estimation of the uncertainty component and total noise. This method is built on Taylor series analysis for the state vector and does not use a mathematical model of the system at all. It allows feedback linearization, as well as intelligent stabilization of the system. This article’s content uses a 2-DOF flat robot implemented on MatlabR2022b software to verify the theory. These findings indicate that superior tracking performance is achievable.

Learning Global Evapotranspiration Dataset Corrections from a Water Cycle Closure Supervision

Evapotranspiration (E) is one of the most uncertain components of the global water cycle (WC). Improving global E estimates is necessary to improve our understanding of climate and its impact on available surface water resources. This work presents a methodology for deriving monthly corrections to global E datasets at 0.25∘ resolution. A principled approach is proposed to firstly use indirect information from the other water components to correct E estimates at the catchment level, and secondly to extend this sparse catchment-level information to global pixel-level corrections using machine learning (ML). Several E satellite products are available, each with its own errors (both random and systematic). Four such global E datasets are used to validate the proposed approach and highlight its ability to extract seasonal and regional systematic biases. The resulting E corrections are shown to accurately generalize WC closure constraints to unseen catchments. With an average deviation of 14% from the original E datasets, the proposed method achieves up to 20% WC residual reduction on the most favorable dataset.

Illustrative Multi‐Centennial Projections of Global Mean Sea‐Level Rise and Their Application

AbstractWe produce projections of global mean sea‐level rise to 2500 for low and medium emissions scenarios (Shared Socioeconomic Pathways SSP1‐2.6 and SSP2‐4.5) relative to 2020, based on extending and combining model ensemble data from current literature. We find that emissions have a large effect on sea‐level rise on these long timescales, with [5, 95]% intervals of [0.3, 4.3]m and [1.0, 7.6]m under SSP1‐2.6 and SSP2‐4.5 respectively, and a difference in the 95% quantile of 1.6 m at 2300 and 3.3 m at 2500 for the two scenarios. The largest and most uncertain component is the Antarctic ice sheet, projected to contribute 5%–95% intervals of [−0.1, 2.3]m by 2500 under SSP1‐2.6 and [0.0, 3.8]m under SSP2‐4.5. We discuss how the simple statistical extensions used here could be replaced with more physically based methods for more robust predictions. We show that, despite their uncertainties, current multi‐centennial projections combined into multi‐study projections as presented here can be used to avoid future “lock‐ins” in terms of risk and adaptation needs to sea‐level rise.

Dynamic modeling and rotation control for flexible single-link underwater manipulator considering flowing water environment based on modified morison equation

Flexible single-link underwater manipulator (FSLUM) can be installed in autonomous underwater vehicles, it can carry out underwater tasks such as offshore oil and gas exploration, underwater cable maintenance as well as fishery farming and fishing. The design and development of the FSLUM are of great significance for ocean exploration and development. The FSLUM is affected by the underwater interference torque during underwater tasks in the flowing water environment, which will greatly affect the operational accuracy. In addition, many nonlinear factors such as the flexibility of the transmission system and the two-dimensional deformation of flexible links will aggravate the control difficulty of the FSLUM. An improved sliding mode control (SMC) strategy based on neural network identification is proposed to enhance the FSLUM's control accuracy. Neural networks are proposed to identify uncertain components including underwater interference and flexible nonlinear terms, so as to advance the control law design accuracy. Firstly, the FSLUM dynamic equations coupled with underwater interference torque considering multiple nonlinear factors are established according to the assumed mode method (AMM). A modified Morison equation is proposed to abbreviate the underwater interference torque in flowing water environments. Next, the control law of improved neural network sliding mode control (NNSMC) strategies are proposed according to the Lyapunov stability theorem, so as to ensure closed-loop stability of the FSLUM. Finally, the results of simulation and prototype tracking control experiments show that the improved NNSMC strategy has high tracking accuracy.

Neural network model identification control of dual-inertia system with a flexible load considering payload mass variation and nonlinear deformation

The dual-inertia system with a flexible load (DSFL) is a complex nonlinear model, which originates from the flexible load deformation and payload mass variation. In dynamic modeling, it is impossible to consider all the nonlinear factors, including high-order modes and inaccurate friction torque, which leads to the existence of uncertain components in the DSFL. Based on this, it is impossible to build a dynamic model of the DSFL that is completely consistent with the real physical model. This modeling errors increases the tracking errors of the DSFL. In this study, the neural networks are employed to recognize the lumped uncertain components of the DSFL dynamic model, so as to reduce the flexible load's tracking error. Firstly, based on the deformation accuracy comparison, a relatively high precision model simplification method is proposed, which neglects the second order mode. Next, the control law of the neural network identification control strategy is designed according to the stability theorem. Finally, through physical control experiments and simulation analysis, it is shown that this control strategy enhances the motion accuracy of the DSFL. The experimental results show that this control strategy predicted by the radial basis function (RBF) neural network can reduce the rotation angle error by 15.86%.

An Aerosol Optical Module With Observation‐Constrained Black Carbon Properties for Global Climate Models

AbstractAtmospheric black carbon (BC) aerosols have been long‐lasting uncertain components in environmental and climate studies. Global climate models (GCMs) potentially overestimate BC absorption efficiency due to a lack of consideration of complex BC microphysical and mixing properties. We extract multiple BC properties from observations and develop an aerosol optical module known as Advanced Black Carbon (ABC) in the framework of the Modal Aerosol Model version 4 (MAM4). The ABC module is implemented in the Community Atmosphere Model version 6 (CAM6) and evaluated by in situ and remote sensing observations. CAM6‐ABC addresses the shortcomings of CAM6‐MAM4 in terms of BC microphysical and mixing properties, particularly their size, mixing state and optical simulations. Sensitivity simulations show that the global BC absorption aerosol optical depth at 550 nm simulated by CAM6‐ABC is reduced by ∼29% compared with that in CAM6‐MAM4. The BC absorption enhancement simulated by CAM6‐ABC is reduced from ∼2.6 of the default MAM4 to ∼1.4, which is closer to the observed values (mostly less than 1.5). With improved BC absorption estimation, the biases of aerosol single‐scattering coalbedo simulations are reduced by 18%–69% compared with global Aerosol Robotic Network observations. Moreover, the globally averaged BC direct radiative effect is reduced from 0.37 to 0.28 W/m2 at the top of the atmosphere. Our new scheme alleviates the overestimation of BC absorption in GCMs by constraining BC microphysical and mixing properties when assessing aerosol radiative and climate effects, and it can be easily implemented in most modal‐based aerosol modules of climate models.

Computing the reliability of mixed uncertain random [formula omitted]-out-of-[formula omitted] systems with multiple possible states

This paper presents an uncertain random multi-state k-out-of-n system (urm-k-out-of-n system, for short), where both random components and uncertain components coexist. States of the system and components display multiplicity and are defined as uncertain random variables. Since reliability is a crucial index to reflect system performance, this paper proves some formulas to calculate the reliability of urm-k-out-of-n systems in accordance with chance theory, which have two special cases, random systems and uncertain systems. Later, a mixed algorithm for multi-valued decision diagram (MDD) and binary search is designed to assess the reliability index. Compared to the general algorithm, it operates in significantly less time when the system becomes complex.

Comparison of Data-Driven Groundwater Recharge Estimates with a Process-Based Model for a River Basin in the Southeastern USA

Reliable estimates of aquifer recharge have the potential to help develop sustainable groundwater management policies. Despite its importance, quantifying this flux continues to be a challenge and remains one of the most uncertain components of the hydrological cycle. Here, we obtain a spatially explicit estimate of recharge using a semi-distributed hydrologic model for a major river basin in the Southeastern United States. A comparison of these process-based estimates with a data-driven recharge product (developed by USGS), which was obtained using a set of empirical regression equations, shows good agreement at the basin scale, but significant discrepancies at finer spatial resolutions. Overall, the semi-distributed model shows a higher degree of spatial heterogeneity across the basin than the USGS study results, which likely indicates that the empirical relationships modeled at the basin scale by the USGS empirical equations might not hold at smaller spatial scales. However, more ground-truthing recharge datasets are necessary to properly evaluate subbasin-scale models and reduce the uncertainty of estimates at these scales.

Mass Balances of the Antarctic and Greenland Ice Sheets Monitored from Space

AbstractSatellite data have revealed that the Greenland and Antarctic Ice Sheets are changing rapidly due to warming air and ocean temperatures. Crucially, Earth Observations can now be used to measure ice sheet mass balance at the continental scale, which can help reduce uncertainties in the ice sheets’ past, present, and future contributions to global mean sea level. The launch of satellite missions dedicated to the polar regions led to great progress towards a better assessment of the state of the ice sheets, which, in combination with ice sheet models, have furthered our understanding of the physical processes leading to changes in the ice sheets' properties. There is now a three-decade-long satellite record of Antarctica and Greenland mass changes, and new satellite missions are planned to both continue this record and further develop our observational capabilities, which is critical as the ice sheets remain the most uncertain component of future sea-level rise. In this paper, we review the mechanisms leading to ice sheets' mass changes and describe the state of the art of the satellite techniques used to monitor Greenland’s and Antarctica’s mass balance, providing an overview of the contributions of Earth Observations to our knowledge of these vast and remote regions.

The Reliability Analysis of a Jacket Platform under Ice Loads Based on Respond Surface Method

Objective: The jacket platform is made of various types of steel pipes and beams welded, mainly used in shallow water areas, such as Bohai oilfield in China. In winter, the sea area where the platform is located will freeze, and the influence of ice load on the platform can not be ignored. So the influence of ice load on the reliability of jacket platform is studied in this paper. Methods: In this research, the finite element model of the jacket platform was established in the form of beam element, and the loads were applied from different directions to find the load direction with the greatest influence on the platform state. When calculating the ultimate load of the platform structure, it is necessary to carry out nonlinear analysis. Selecting the ice load condition, increasing the ice load step by step on the platform structure. As the applied load gradually increases, the platform will appear nonlinear, then the ultimate load of the jacket platform will be obtain. The pile radius and pile thickness are uncertain components in the reliability study of the jacket platform due to manufacture, processing, and other factors, therefore the values of the pile's parameters are all random. Consider the deviation of geometric parameters with the extreme value type I distribution of ice load, the limit state equation for the strength failure of the platform under ice loads is obtained using Response Surface Method, then the reliability of the platform is calculated by the Monte Carlo method and First Order Second Moment Reliability method. Results: By nonlinear analysis, the ultimate bearing capacity of the platform studied in this paper is about 8000kN. The reliability probability of the platform calculated by Monte Carlo method is 0.9999993, and the reliability index is 4.813. The reliability probability of the platform calculated by the first order second moment method is 0.9999994, and the reliability index is 4.852. Conclusion: The results obtained by the two methods are almost the same, and the reliability index is very large, indicating that the platform can operate safely and reliably under the ice load.

Spatial and temporal variability in summertime dissolved carbon dioxide and methane in temperate ponds and shallow lakes

AbstractSmall waterbodies have potentially high greenhouse gas emissions relative to their small footprint on the landscape, although there is high uncertainty in model estimates. Scaling their carbon dioxide (CO2) and methane (CH4) exchange with the atmosphere remains challenging due to an incomplete understanding and characterization of spatial and temporal variability in CO2 and CH4. Here, we measured partial pressures of CO2 (pCO2) and CH4 (pCH4) across 30 ponds and shallow lakes during summer in temperate regions of Europe and North America. We sampled each waterbody in three locations at three times during the growing season, and tested which physical, chemical, and biological characteristics related to the means and variability of pCO2 and pCH4 in space and time. Summer means of pCO2 and pCH4 were inversely related to waterbody size and positively related to floating vegetative cover; pCO2 was also positively related to dissolved phosphorus. Temporal variability in partial pressure in both gases weas greater than spatial variability. Although sampling on a single date was likely to misestimate mean seasonal pCO2 by up to 26%, mean seasonal pCH4 could be misestimated by up to 64.5%. Shallower systems displayed the most temporal variability in pCH4 and waterbodies with more vegetation cover had lower temporal variability. Inland waters remain one of the most uncertain components of the global carbon budget; understanding spatial and temporal variability will ultimately help us to constrain our estimates and inform research priorities.

Mass balance of the Greenland and Antarctic ice sheets from 1992 to 2020

Abstract. Ice losses from the Greenland and Antarctic ice sheets have accelerated since the 1990s, accounting for a significant increase in the global mean sea level. Here, we present a new 29-year record of ice sheet mass balance from 1992 to 2020 from the Ice Sheet Mass Balance Inter-comparison Exercise (IMBIE). We compare and combine 50 independent estimates of ice sheet mass balance derived from satellite observations of temporal changes in ice sheet flow, in ice sheet volume, and in Earth's gravity field. Between 1992 and 2020, the ice sheets contributed 21.0±1.9 mm to global mean sea level, with the rate of mass loss rising from 105 Gt yr−1 between 1992 and 1996 to 372 Gt yr−1 between 2016 and 2020. In Greenland, the rate of mass loss is 169±9 Gt yr−1 between 1992 and 2020, but there are large inter-annual variations in mass balance, with mass loss ranging from 86 Gt yr−1 in 2017 to 444 Gt yr−1 in 2019 due to large variability in surface mass balance. In Antarctica, ice losses continue to be dominated by mass loss from West Antarctica (82±9 Gt yr−1) and, to a lesser extent, from the Antarctic Peninsula (13±5 Gt yr−1). East Antarctica remains close to a state of balance, with a small gain of 3±15 Gt yr−1, but is the most uncertain component of Antarctica's mass balance. The dataset is publicly available at https://doi.org/10.5285/77B64C55-7166-4A06-9DEF-2E400398E452 (IMBIE Team, 2021).