Search

The fault diagnosis of rotating machinery is vital in industry but traditionally depends on manual expertise, requiring substantial resources. To improve diagnostic accuracy, enable effective condition monitoring, and minimize the impact of faults on operations, advanced diagnostic techniques are essential. Hence, we propose an advanced fault diagnosis framework that leverages improved particle swarm optimization (IPSO), variational mode decomposition (VMD), and probabilistic neural networks (PNN) to accurately diagnose faults in rotating machinery using gear and rolling bearing vibration signals. Initially, the vibration signals are decomposed into intrinsic mode functions via VMD, enabling the capture of subtle but critical fault features. To address parameter selection challenges in VMD, we employed IPSO to optimize the VMD parameters, ensuring the optimal decomposition effect. Further, we refined the feature set by applying Laplace fraction optimization and feature dimensionality reduction, isolating sensitive features that serve as input to a PNN-based fault classification model. Experimental results demonstrated that this IPSO-VMD-PNN framework achieves high diagnostic accuracy for various fault types, establishing it as an effective tool for fault identification in rotating machinery. [ABSTRACT FROM AUTHOR]

Logs in the petroleum boreholes indirectly records the sedimentary cycles in the deep burial formation. In order to extract and understand the periodicity and cyclicity, it is necessary to process the data by digital signal analysis method. Taking the gamma ray (GR) log as the primary material, an identification approach of Milankovitch cycles in boreholes is proposed in this paper, which is based on the Maximum Entropy Spectral Analysis (MESA). The first stage chooses the appropriate windows for calculating the frequency spectral properties in a short section of the data. In each depth window, the second stage generates the two-dimension frequency spectrum utilizing the MESA. At each depth point, the third stage finds the potential Milankovitch cycles in the one-dimension frequency spectrum, in which the average amplitude spectrum peak would be matched to the ratio of Milankovitch period. According to the frequency and wavelength of the maximum amplitude in Milankovitch cycles, the fourth stage estimates the sedimentation rate controlled by cyclical factor. Finally, the Milankovitch cycles in Lower Member of Miocene Zhujiang Formation in north slope of Baiyun Sag, Pearl River Mouth Basin, are identified and the cyclical sedimentation rate is estimated. The results demonstrate that the proposed method is feasible and effective to identify Milankovitch cycles in boreholes, which may contribute to the other geological researches.

The Jinghong mafic–ultramafic complex, exposed in the eastern margin of the Lancangjiang tectonic belt, is related to the subduction of the Paleo‐Tethys Ocean. Its petrogenesis plays a key role in constraining the tectonic evolution of the eastern Paleo‐Tethys Ocean in southwestern China. In this study, we present petrological, geochemical and geochronological results of the Jinghong complex rocks, in order to decipher their origin and tectonic significance. The Jinghong mafic–ultramafic complex was composed of peridotite, gabbro, basalt and minor plagiogranite. Whole‐rock geochemical data of the mafic rocks indicate that they have both MORB and IAB affinities and plot in the back‐arc basin basalt (BABB) field in the FeO∗/MgO vs. TiO2 diagram. Combined with their trace element characteristics, it can be concluded that the Jinghong mafic–ultramafic complex represents an ophiolite suite that was formed in a back‐arc ocean basin. Precise LA‐ICP‐MS zircon U‐Pb dating yielded weighted mean 206Pb/238U ages of 298.4 ± 1.7 Ma, 294.3 ± 1.6 Ma, and 292.8 ± 2.0 Ma for gabbroic rocks from this complex, which indicates that the Jinghong ophiolites were formed during the early Permian (298–293 Ma). We propose that during subduction of the main Paleo‐Tethys Ocean, a back‐arc ocean basin was formed at the east of the Lancangjiang tectonic belt. [ABSTRACT FROM AUTHOR]

The Neoarchean basement rocks of the Qixingtai region in the northwest of western Shandong, eastern North China Craton are divided into western and eastern domains, separated by a ductile shear zone. The western domain is mainly composed of early Neoarchean tonalitic and supracrustal rocks, and the eastern domain is composed of late Neoarchean crustally‐derived granite with diverse early Neoarchean enclaves up to several kilometres long of supracrustal and gneissic TTG‐diorite rocks. This study reports an integrated field, whole‐rock geochemistry and zircon U–Pb‐Hf study on the granites and their enclaves in the eastern domain. The supracrustal enclaves are mainly composed of meta‐basaltic and meta‐ultramafic rocks and are the same in lithological association and formation time as the 2,750–2,700 Ma Liuhang "Formation" in the western area. The TTG‐diorite enclaves can be divided into early and late groups. Magmatic zircons from early TTG‐diorite samples have U–Pb ages of 2,731 ± 17 to 2,748 ± 13 Ma and εHf(t) values of +2.7 to +9.5, whereas trondhjemite samples of the late group have U–Pb zircon ages of 2,588 ± 18 to 2,606 ± 12 Ma and εHf(t) values of +0.6 to +6.5. The Hf isotopic signatures on magmatic zircon from the >2,700 Ma diorite‐tonalites indicate that they are predominantly juvenile crustal additions, probably with minor involvement of older crust in their genesis, as given by tDM (Hf) ages ranging back to 2.92 Ga. The eastern granites range from monzogranite to syenogranite in composition and commonly have negative Eu anomalies, indicating residual plagioclase in the source region. Magmatic zircons from two granite samples have U–Pb ages of 2,516 ± 24 and 2,527 ± 18 Ma and εHf(t) values of +1.0 to +7.0. A fine‐grained syenogranite dyke contains inherited early Neoarchean zircons with εHf(t) values of +1.6 to +7.4. The isotopic signatures of the late Neoarchean granites that dominate the eastern domain show that they were derived from partial melting recycling of older continental crust recognized in the region – early Neoarchean rocks formed as a result of juvenile crustal additions. This is a common signature for late Archean crustal evolution throughout much of the North China Craton, and is the hallmark of some other Archean terranes, such as the Dharwar Craton of south India. The geodynamic explanations for the formation globally of vast amounts of crustally‐derived late Neoarchean granite are discussed. [ABSTRACT FROM AUTHOR]

Satisfying the process technical requirements while optimizing the control of oxygen and zinc oxide is the main task for optimal operation of the iron removal process. However, due to the complicated mechanism and varied production conditions, the process is difficult to achieve optimal operation with the simple controller. The manual control, as a result, is extensively used in practice. In this paper, we develop an optimal setting and control (OSC) system for the iron removal process to achieve its technical requirements with minimal process consumptions. The OSC system is composed of a neural network soft-sensor, an optimal setting module, an optimal controller, and a fuzzy-logic-based compensator. First, we define the oxygen reaction efficiency (ORE) to measure the difference between the theoretical oxygen amount and its actual amount. Due to the ORE cannot be measured online and it varies with the production conditions, an adaptive weight radial basis function neural network soft-sensor is designed to estimate it. The adaptive weight adjusting method contributes to improve the adaptability of the soft-sensor, and its convergence is discussed. The optimal setting module provides the set-point of outlet ferrous ion concentration for the optimal operation of every reactor. The steady-state optimal control of oxygen and zinc oxide is then established, and the compensator compensates the control inputs utilizing the feedforward and feedback information. Finally, simulations validate that the ORE is important for the optimal control of the process. Furthermore, industrial experiments are presented to verify the effectiveness and potential of the proposed strategy. [ABSTRACT FROM AUTHOR]

In this paper, a novel three-layer coordinating optimization and control strategy involving fuzzy neural networks (FNNs) is proposed for industrial plant-wide control. This strategy includes a coordinator (top layer), local optimizers (middle layer), and local controllers (bottom layer). The coordinator determines and coordinates set-points of production indices for each process in the plant, aiming to achieve plant-wide optimal control. The local optimizers optimize individual unit and deliver the information to the coordinator. The controllers calculate optimal control inputs for the processes according to the set-points. Model predictive control (MPC) is used in this layer. Also used are FNNs with a gradient-based learning algorithm, which are trained to represent the models of the processes that are controlled by MPC. By constructing a Lyapunov function, convergence of the FNNs is established. Experiments are carried out in the largest zinc hydrometallurgy plant in China. The results demonstrate performance of our strategy in tracking the set-points and saving processing costs. Moreover, the strategy achieves a more optimal overall plant operation when compared to the same FNN-based MPC scheme that does not use the coordinator. [ABSTRACT FROM AUTHOR]

Abundant late Neoarchean granitoids occur in southwestern Liaoning Province, part of the Eastern Ancient Terrane of the North China Craton. These rocks include intermediate gneiss, TTG gneisses and potassic granitoids, and we report on the geochemistry and zircon SHRIMP ages as well as Hf-in-zircon isotopes of these granitoids in order to determine their petrogenesis. Field relationships suggest that most of these granitoids experienced widespread metamorphism and deformation, associated with anatexis at some localities. The intermediate gneisses, TTG gneisses and potassic granitoids were all emplaced at the end of the Neoarchean (2.50–2.53 Ga), and CL images document widespread recrystallization in the zircons. The intermediate and TTG gneisses yielded similar Hf isotopic systematics (ε Hf(t) = −3.73 to +6.42) as the associated potassic granitoids (ε Hf(t) = −2.44 to +7.80), and both rock types yielded mean Hf crustal model ages of 2.8–2.9 Ga. Combined with the geochemistry, we propose that the formation of the intermediate and TTG gneisses was related to partial melting of mafic rocks at different depth, whereas the potassic granitoids have variable petrogenesis. The nearly coeval TTG gneisses and potassic granitoids and their widespread metamorphism, deformation and zircon recrystallization suggest that a large-scale heat source must have been present at or near the base of the crust in southwestern Liaoning Province at the end of the Neoarchean. We propose that collision and post-collisional extension is the most likely tectonic environment for generation of the above granitoids, and the formation of widespread potassic granitoids played an important role in the maturation of continental crust in the North China Craton. Unlabelled Image • TTG gneisses and potassic granitoids show indistinguishable formation ages and Hf isotopes. • Widespread zircon recrystallization occurred immediately after formation of these granitoids. • A long-lived heat source is necessary for the formation and metamorphism of these granitoids. • Post-collision extension is the most likely environment for these granitoids. [ABSTRACT FROM AUTHOR]

An adaptive generalized predictive control (GPC) scheme for an industrial process is designed based on a neuron adaptive splitting and merging radial basis function neural network (NASM-RBFNN) in this paper. The NASM-RBFNN is developed to identify the dynamic behaviors of the industrial process. In order to provide an accurate prediction model for the GPC, a neuron adaptive splitting and merging strategy and a weighted parameters adaptive correction approach are proposed. The neuron adaptive splitting and merging strategy can automatically add or delete the hidden neurons on-line, and the weighted parameters adaptive correction approach can update the weights based on the error of the neural network. The proposed approaches can enable the NASM-RBFNN to be adapted to the time-varying production condition and stochastic disturbance. The stability analysis and convergence of neuron adaptive splitting and merging strategy and weighted parameters adaptive correction approach are given. Finally, the NASM-RBFNN-based GPC (NASM-RBFNN-GPC) is applied to control the iron removal process in the largest zinc hydrometallurgy plant in China. The industrial experiments demonstrate that the NASM-RBFNN-GPC has a satisfactory tracking and control performance. [ABSTRACT FROM AUTHOR]

In the iron removal process, which is composed of four cascaded reactors, outlet ferrous ion concentration (OFIC) is an important technical index for each reactor. The descent gradient of OFIC indicates the reduced degree of ferrous ions in each reactor. Finding the optimal descent gradient of OFIC is tightly close to the effective iron removal and the optimal operation of the process. This paper proposes a coordinated optimization strategy for setting the descent gradient of OFIC. First, an optimal setting module is established to determine the initial set-points of the descent gradient. The oxygen utilization ratio (OUR), an important parameter in this module, cannot be measured online. Therefore, a self-adjusting RBF (SARBF) neural network with an adaptive learning rate is developed to estimate the OUR. The convergence of the SARBF neural network is discussed. Then, a coordinated optimization strategy is proposed to adjust the set-points of the descent gradient when the measured OFICs drift away from their desired set-pints. If the final OFIC does not satisfy the process requirements, a compensation mechanism is developed to provide a compensation for the set-points of the descent gradient. Finally, industrial experiments in the largest zinc hydrometallurgy plant validate the effectiveness of the proposed coordinated optimization strategy. Our strategy improves the qualified ratio of the OFIC and the quality of the goethite precipitate. More profit is created to the iron removal process after our strategy is applied. [ABSTRACT FROM AUTHOR]

As part of the zinc hydrometallurgy plant, the iron removal process is a complex system with four cascaded reactors. Tighter process-index control is difficult to achieve due to the complicated, long, and time-varying removal process. The control performance is also affected by the quality of the ore source and external disturbances. Little research is documented in the literature to address these difficulties and manual control is widely used. An innovative hybrid control strategy is developed to control the iron removal process’ indices within narrow ranges with minimum cost of additive regents, including oxygen and zinc oxide. This strategy is composed of an optimal setting model, a model-based optimal controller, an integrated prediction model, a fuzzy-logic-based feedforward compensator, and a model feedback adjustor. The optimal setting model automatically optimizes the set-points of the process indices under different production conditions. To achieve the process requirements with minimal cost, the model-based optimal controller is designed. The integrated prediction model is established to provide a more accurate on-line prediction of the process indices by integrating the mechanism prediction model and an error compensation model based on the least-square support vector machine. Based on the predicted process indices, the compensator is developed for the optimal controller. The adjustor provides a parameter adjustment mechanism. Four-week-long industrial experiments in the largest zinc hydrometallurgy plant in China show that the control strategy can not only improve the process-indexes control performance, but also save 6.55% oxygen and 4.61% zinc oxide consumptions, which translates to 222 858 m3oxygen and 1236 t zinc oxide per year (a saving of about $570 000). The hybrid control strategy can be extended to cover other similar processes in the zinc hydrometallurgy and other industries. [ABSTRACT FROM AUTHOR]

The North China Craton is an ideal place for studying the transition of the Earth's thermal structure and tectonics at the Archean–Proterozoic boundary due to its good preservation of the ~ 2.5 Ga tectono-thermal events. We report the discovery of a high-pressure mafic granulite from the Jiaodong Terrain in the North China Craton. The mafic granulite occurs as garnet–clinopyroxene–orthopyroxene–hornblende gneiss enclaves within a late-Archean trondhjemite–tonalite–granodiorite (TTG) gneiss. Typical high-pressure mineral assemblage of garnet–clinopyroxene–plagioclase–quartz ± rutile has been identified. Plagioclase + clinopyroxene ± orthopyroxene ± hornblende symplectite surrounding garnet (“white eye”) is also observed. Using the conventional geothermobarometry and the pseudosection modeling, a clockwise metamorphic P–T path with the peak conditions at ~ 17 kbar and ~ 880 °C was determined. Zircon U–Pb analyses (SHRIMP) on the overgrowth rim of zircon grains of two samples from the same outcrop yielded a metamorphic age of 2473 ± 6 Ma (MSWD = 0.8). The analyses on magmatic core gave a probable magmatic age of 2527 ± 12 Ma (MSWD = 1.9). The high-pressure granulite facies metamorphism corresponds to a collisional event between the ~ 2.5 Ga crust and ~ 2.9 Ga crust at the dawn of Paleoproterozoic in the North China Craton. It also represents a new but rare case of a subduction–collision tectonics at the Archean–Proterozoic transition and provides insight into the change of the Earth's thermal structure. [ABSTRACT FROM AUTHOR]

Iron precipitation by goethite plays an important role in zinc hydrometallurgy. The ferrous ion concentration, which is a key index for assessing the iron removal rate and process control results, cannot be measured on-line. In this study, an integrated predictive model of the ferrous ion concentration is established by integrating the mechanism model and error compensation model, which is based on data identification. The mechanism model is proposed based on an analysis of the process reaction and considering the reaction unit as a continuous stirred tank reactor model. For unknown parameters in the mechanism model, a double-particle swarm optimization algorithm based on information exchange and dynamic adjustment of the feasible region is developed for optimal selection. To improve the adaptive capability of the integrated model, we propose a model-updating strategy and parameter calibration method based on a sensitivity analysis to accomplish on-line adaptive updating of the predictive model. The simulation results demonstrate that the proposed model can effectively track the variation tendency of the ferrous ion concentration and successfully improve the adaptability of the integrated model. [ABSTRACT FROM AUTHOR]