The network trend of isolated communities adds urgency to accelerate the deployment of community integrated energy systems (CIES). CIES effectively combines and optimizes multiple energy systems, leveraging their complementarity for efficient utilization and economical energy supply. However, the escalating intricacies of coupling multiple energy sources and the rising system uncertainties both pose challenges to flexibility scheduling of energy supply and demand. Additionally, the potential flexibility of building thermal inertia and pipeline gas linepack in diverse CIES, encompassing residential, commercial, and industrial communities, remains unexplored. To tackle these issues, a stochastic model predictive control (SMPC) based multi-temporal-spatial-scale flexibility scheduling strategy considering multiple uncertainty sources and system inertia components is proposed. First, the optimization model of CIES is formulated to improve operational flexibility and efficiency, resolve ene... [more]

Fractured-vuggy carbonate reservoirs are tectonically complex; their reservoirs are dominated by holes and fractures, which are extremely nonhomogeneous and are difficultly exploited. Conventional water injection can lead to water flooding, and the recovery effect is poor. This paper takes the injection of foam and solid particles to control bottom water as the research direction. Firstly, the rheological properties of foam were studied under different foam qualities and the presence of particles. The ability of foam to carry particles was tested. By designing a microcosmic model of a fractured-vuggy reservoir, we investigated the remaining oil types and the distribution caused by bottom water. Additionally, we analyzed the mechanisms of remaining oil mobilization and bottom water plugging during foam flooding and foam−particle co-injection. The experimental results showed that foam was a typical power-law fluid. Foam with a quality of 80% had good stability and apparent viscosity. Dur... [more]

Instability in coal pillars and filling bodies is a common occurrence during the mining process of continuous mining and continuous backfilling (CMCB). In view of this, combining numerical simulation, similarity simulation, and on-site testing approaches, backfill mining models were established in Flac3d5.01 software, similarity model test bench, and “two-stage”, “three-stage”, and “four-stage” mining sequences were conducted; the stress characteristics of coal pillar-filling body and the displacement evolution law of surrounding rock have been compared under three typical mining sequences. The results show that compared to two-stage mining sequence, three-stage and four-stage mining sequences provide sufficient time for the solidification of the filling body. The coal pillar exhibits better stability in the early stage of mining, but the stress concentration phenomenon is more significant in the later stage of mining. The stress concentration coefficient is the highest when the width... [more]

Bearing, as one of the core parts of rotating machinery, has a running state which is related to the overall operation of the system. Due to the bearing structure and its complex operating environment, running condition monitoring and fault diagnosis is always a key problem in the field of bearing health management, which is of great significance for bearing maintenance and equipment reliability and safety. In view of the difficulty in parameter selection and poor feature extraction ability of variational mode decomposition (VMD) in existing feature extraction, this paper uses the flying squirrel search algorithm (SSA) to optimize the parametric of decomposition layer k and penalty factor α in VMD, and forms an adaptive VMD signal decomposition method. To solve the problem of high dimensionality and long extraction time of multi-domain fault feature set, kernel principal component analysis (KPCA) is used to reduce feature dimensionality. Then, the processed features are input into the... [more]

With the introduction of numerous technologies and equipment, the volume of data in smart substations has undergone exponential growth. In order to enhance the intelligent management level of substations and promote their efficient and sustainable development, the one-key sequential control system of smart substations is being renovated. In this study, firstly, the intelligent substation is defined and compared with the traditional substation. The one-key sequential control system is introduced, and the main issues existing in the system are analyzed. Secondly, experiments are conducted on the winding temperature, insulation oil temperature, and ambient temperature of power transformers in the primary equipment. Combining data fusion technology and transformer neural network models, a Power Transformer-Transformer Neural Network (PT-TNNet) model based on data fusion is proposed. Subsequently, comparative experiments are conducted with multiple algorithms to validate the high accuracy,... [more]

Traditional batch ethanol fermentation poses the problems of poor production and economic viability because the lag and stationary phase always demand considerable fermentation time; plus, downtime between batches is requested to harvest, clean, and sterilize, decreasing the overall productivity and increasing labor cost. To promote productivity and prolong the production period, avoid process instability, and assure a substantial production of ethanol and a minimal quantity of residual substrate, this paper proposed a nonlinear adaptive control which can realize global stabilizing control of the process starting from batch mode to achieve batch/washout avoidance. Due to the dynamic nature and complexity of the process, novel estimation and control schemes are designed and tested on an ethanol fermentation model. These schemes are global stabilizing control laws including adaptive control to avoid input saturation, nonlinear estimation of the unknown influential concentration through a... [more]

The power battery constitutes the fundamental component of new energy vehicles. Rapid and accurate fault diagnosis of power batteries can effectively improve the safety and power performance of the vehicle. In response to the issues of limited generalization ability and suboptimal diagnostic accuracy observed in traditional power battery fault diagnosis models, this study proposes a fault diagnosis method utilizing a Convolutional Block Attention Capsule Network (CBAM-CapsNet) based on a stacked sparse autoencoder (SSAE). The reconstructed dataset is initially input into the SSAE model. Layer-by-layer greedy learning using unsupervised learning is employed, combining unsupervised learning methods with parameter updating and local fine-tuning to enhance visualization capabilities. The CBAM is then integrated into the CapsNet, which not only mitigates the effect of noise on the SSAE but also improves the model’s ability to characterize power cell features, completing the fault diagnosis... [more]

To enhance the fault diagnosis capability for ultrasonic liquid flow meters and refine the fault diagnosis accuracy of support vector machines, we employ Levy flight to augment the global search proficiency. By utilizing circle chaotic mapping to establish the starting locations of sparrows and refining the sparrow position with the highest fitness value, we propose an enhanced sparrow search algorithm termed CLSSA. Subsequently, we optimize the parameters of support vector machines using this algorithm. A support vector machine classifier based on CLSSA has been constructed. Given the intricate data collected from ultrasonic liquid flow meters for diagnostic purposes, the approach of employing KPCA to decrease data dimensionality is implemented, and a KPCA-CLSSA-SVM algorithm is proposed to achieve fault diagnosis in ultrasonic flow meters. By using UCI datasets, the findings indicate that KPCA-CLSSA-SVM achieves fault diagnosis accuracies of 94.12%, 100.00%, 97.30%, and 100% in the f... [more]

Industrial control systems are often used to assist and manage an industrial operation. These systems’ weaknesses in the various hierarchical structures of the system components and communication backbones make them vulnerable to cyberattacks that jeopardize their security. In this paper, the security of these systems is studied by employing a reinforcement learning extended attack graph to efficiently reveal the subsystems’ flaws. Specifically, an attack graph that mimics the environment is constructed for the system using the state−action−reward−state−action technique, in which the agent is regarded as the attacker. Attackers may cause the greatest amount of system damage with the fewest possible actions if they have the highest cumulative reward. The worst-case assault scheme with a total reward of 42.9 was successfully shown in the results, and the most badly affected subsystems were recognized.

Water quality deterioration and eutrophication have become a global concern, while reservoir pollution caused by multiple factors has led to frequent algal blooms, posing a serious threat to rural drinking water security and urban water supply. The purpose of this paper is to analyze the current water quality of Waihu Reservoir and use the single index method, the weighted comprehensive scoring method, and the nutrient level index method (TLI) to evaluate eutrophication. On this basis, the pollution sources of the reservoir are comprehensively analyzed and discussed, and effective control strategies are proposed. The evaluation results indicate that the reservoir is of moderate eutrophication type. Therefore, reducing the input of nutrients such as nitrogen and phosphorus in water is the main goal of alleviating exogenous pollution. The combination of engineering intervention and ecological restoration strategies to remove nutrients from the aquatic environment is an effective strategy... [more]

In view of the high salinity characteristics of reinjection oilfield wastewater in the Gasi Block of Qinghai Oilfield, with the polymer produced by Shandong Baomo as the research target, we systematically investigated the variations in the impact of six ions, Na+, K+, Ca2+, Mg2+, Fe2+, and Fe3+, in the produced water from polymer flooding on the viscosity and stability of the polymer solution. Additionally, we provided the primary research methods for complexation in reinjected wastewater. Experimental results indicate that the main factors leading to a decrease in polymer viscosity are high-valence cations, with the descending order of their influence being Fe2+ > Fe3+ > Mg2+ > Ca2+ > Na+ > K+. High-valent cations also effect the viscosity stability of polymer solutions, and their order from greatest to least impact is: Fe2+ > Ca2+(Mg2+) > Fe3+ > Na+(K+). This article is focused on investigating the influencing factors and extent of the impact of oilfield wastewater on the viscosity o... [more]

There has been a tendency in oil and gas industry towards the adoption of multilateral wells (MLWs) with completions that incorporate multiple types of flow control devices (FCDs). In this completion technique, passive inflow control devices (ICDs) or autonomous inflow control devices (AICDs) are positioned within the laterals, while interval control valves (ICVs) are installed at lateral junctions to regulate the overall flow from each lateral. While the outcomes observed in real field applications appear promising, the efficacy of this specific downhole completion combination has yet to undergo comparative testing against alternative completion methods that employ a singular flow control device type. Additionally, the design and current evaluations of such completions are predominantly based on analytical tools that overlook dynamic reservoir behavior, long-term production impacts, and the correlation effects among different devices. In this study, we explore the potential of integra... [more]

Given the demand for extremely low sulfur content in 5 wt% Si high-silicon austenitic stainless steel (SS-5Si), smelting utilizes a slag composition of CaF2-CaO-Al2O3-MgO-SiO2 with a basicity of 1 to 3, Al2O3 content ranging from 2.04 to 9.61%, and CaF2 content between 20.8 and 31.62%. Experiments designed to investigate the sulfur content in molten steel at temperatures of 1773 K, 1823 K, and 1873 K over durations of 1, 5, 10, 15, and 30 min, under varying slag compositions, corroborated with a theoretically derived model hypothesizing a “rate-controlling” step in mass transfer, revealed that the mass transfer of sulfur within the molten steel was determined to be the rate-controlling step (RCS) in the (CaO) + [S] = (CaS) + [O] reaction kinetics, and the variability of the mass transfer coefficient of sulfur, kS,m, in the molten steel ranged from 1.04 × 10−5 m∙s−1 to 2.24 × 10−5 m∙s−1. Based on the temperature dependency of kS,m, the apparent activation energy for the desulfurization... [more]

The subsea control system, a pivotal element of the subsea production system, plays an essential role in collecting production data and real-time operational monitoring, crucial for the consistent and stable output of offshore oil and gas fields. The increasing demand for secure offshore oil and gas extraction underscores the necessity for advanced reliability modeling and effective maintenance strategies for subsea control systems. Given the enhanced reliability of subsea equipment due to technological advancements, resulting in scarce failure data, traditional reliability modeling methods reliant on historical failure data are becoming inadequate. This paper proposes an innovative reliability modeling technique for subsea control systems that integrates a Wiener degradation model affected by random shocks and utilizes the Copula function to compute the joint reliability of components and their backups. This approach considers the unique challenges of the subsea environment and the co... [more]

Blowout is one of the most serious safety threats in deepwater drilling. Considering the characteristics of gas invasion in complex formations, gas migration and distribution, and dynamic changes in temperature inside a wellbore, a deepwater well-closing pressure determination method considering thermal-fluid-solid coupling was proposed. The model was verified using actual data, and the average error in the increase in casing pressure during the closing process was found to be 5.42%. The shut-in pressure of oil and gas wells under a transient shut-in process was analyzed. The results showed that the fluid thermal expansion caused by temperature recovery had a significant impact on the change in wellhead backpressure after well closure. Furthermore, the time required for the wellbore pressure to recover to the formation pressure varies nonlinearly with factors such as geothermal gradients, pit gains, bottom-hole pressure, and gas production indices. A pressure calculation chart was deve... [more]

To address the issues of uneven sample lengths in the centrifuge machine bearings of the ternary precursor, inaccurate fault feature extraction, and insensitivity of important feature channels in rolling bearings, a rolling bearing fault diagnosis method based on adaptive sample length adjustment of one-dimensional convolutional neural network (1DCNN) and squeeze-and-excitation network (SeNet) is proposed. Firstly, by controlling the cumulative variance contribution rate in the principal component analysis algorithm, adaptive adjustment of sample length is achieved, reducing data with uneven sample lengths to the same dimensionality for various classes. Then, the 1DCNN extracts local features from bearing signals through one-dimensional convolution-pooling operations, while the SeNet network introduces a channel attention mechanism which can adaptively adjust the importance between different channels. Finally, the 1DCNN-SeNet model is compared with four classic models through experimen... [more]

Many researchers employed Poisson distribution-based control charts to monitor count data. Nevertheless, these charts can handle count data that deviate from the Poisson assumption of equal mean and variance. This paper suggests a new control chart (CC) that uses the generalized Conway−Maxwell−Poisson (GCOMP) distribution, which can deal with count data that have different levels of dispersion and zero-inflation (ZI). The proposed chart is designed considering the total number of counts. The main advantage of this study is that it pays attention to the tails of the count data when monitoring the process. The performance is measured by the average run length using L control limits at different sample sizes and parametric settings. The findings demonstrate that, for count data with varying tail behaviors, the proposed chart performs better compared to existing CCs. ZI count data can also be monitored with the proposed chart. The proposed chart can be applied in a variety of fields, as ve... [more]

Traditional data-driven soft sensor methods can be regarded as an optimization process to minimize the predicted error. When applying the mean squared error as the objective function, the model tends to be trained to minimize the global errors of overall data samples. However, there are deviations in data from practical operation, in which the model performance in the estimation of the local variations in the target parameter worsens. This work presents a solution to this challenge by considering higher-order moments of prediction residuals, which enables the evaluation of deviations of the residual distribution from the normal distribution. By embedding constraints on the distribution of residuals into the objective function, the model tends to converge to the state where both stationary and deviation data can be accurately predicted. Data from the Tennessee Eastman process and an industrial cracking furnace are considered to validate the performance of the proposed modeling method.

The online measurement of the aluminum−silicon ratio of red mud in the dissolution stage of the Bayer alumina production process is difficult to achieve. The offline assay method has a high cost and strong time delay. Soft sensors are an effective and economical method to solve such problems. In this paper, a hybrid model (TPRF model) based on a tree-structured Parzen estimator (TPE) optimized random forest (RF) algorithm is proposed to measure the Al−Si ratio of red mud. The probability distribution of the hyperparameters of the random forest model is estimated by combining the TPE optimization algorithm with the random forest algorithm. According to this probability distribution, the hyperparameters of the random forest algorithm are adjusted in the parameter search space to obtain the best combination of hyperparameters. We established a TPRF soft sensing model based on the optimal combination of hyperparameters. The results show that the best performance of the TPRF model is a mean... [more]

Large amounts of coal fines are discharged from coalbed methane wellheads in the Qinshui Basin, obstructing the continuity of drainage; their extraction poses significant hazards. This paper recognized the coal body structure of 30 coalbed methane wells in the study region, using the integrated identification method of logging curve and tectonic curvature. The research found that the primary structural coal output of coal fines concentration averaged 0.237 g/L, the average content of particle size 10−100 μm was 58.88%, the average range of particle size 1−10 μm was 22.91%, and the main form was irregular columns and lumps. The average concentration of fractured structural coal fines was 1.169 g/L, the average content of particle size 10−100 μm was 41.73%, the average range of particle size 1−10 μm was 31.77%, and the main form was balls and lumps. The average concentration of granulated-mylonitic structured coal fines was 3.156 g/L, the average content of particle size 10−100 μm was 25... [more]

Correlation diagnosis in multivariate process quality management is an important and challenging issue. In this paper, a new approach based on the optimal typical correlated component pair group (OTCCPG) is proposed. Firstly, the theorem of correlation decomposition is proved to decompose the correlation of all the quality components as serial correlations of component pairs, and then according to the transitivity of correlations of component pairs, the decomposition result is represented by a correlation set of typical correlated component pairs. Finally, an algorithm for OTCCPG based on the maximum correlation spanning tree (MCST) is proposed, and T2 control charts to monitor the correlations of component pairs in OTCCPG are established to form the correlation diagnostic system. Theoretical analysis and practice prove that the proposed method could reduce the space complexity of the diagnostic system greatly.

The modular reconfigurable flight array (MRFA) is composed of multiple identical flight unit modules, which has several advantages such as structural variability, strong versatility, and low cost. Due to the redundant properties of MRFA, it keeps stable by adopting a suitable control law when it suffers actuator fault or actively stops some actuators. To address the attitude stability issue of the modular flight array when actuators actively stop or encounter failures during the flight process, a modeling method based on a switched system is proposed at first, and an arbitrary switched controller design method based on the segmented Lyapunov functions and the average dwell time is also given. By introducing the actuator efficiency matrix, the dynamic switched model of the modular flight array is described. Then, a group of arbitrary switched linear feedback gains is designed to ensure the exponential stability of the flight array if the switched process satisfies the constraint of the... [more]

In this work, issues of water separation and purification are addressed, where, in order to achieve the desired results, it is necessary to use several disciplines such as classical physics, biotechnology, automatic control, automation, and applications of industry 4.0. Further, the need for comprehensive and automated solutions for rainwater treatment in the agricultural sector is addressed. This research focuses on designing and implementing a system adapted to these needs using Siemens technologies. The methodology ranges from the design of the Piping and Instrumentation Diagram (P&ID) to the implementation of the interface, incorporating Siemens technologies for data acquisition, electrical connections, treatment programming, and PID controller design. The results show significant advances in the development of the system, highlighting the effectiveness of automation and the HMI-PLC human−machine interface in process monitoring and control. These findings support the viability of a... [more]

In order to apply precipitated calcium carbonate (PCC) in the detergent industry, its ability to deposit calcium ions in hard water is an important process. In this work, the calcium ion deposition in the presence of PCC from different sources is investigated to reveal the influencing factors and mechanism of nucleation and crystal growth of CaCO3. SEM, XRD, Malvern particle size analysis, and calcium electrodes are used to evaluate the effects of PCC morphology, saturation of Ca2+, and PCC additive amount on the deposition behavior of CaCO3. Through SEM and Malvern particle size analysis, it is found that the precipitation of calcium ions is obviously accelerated by PCC acting as seeds. Moreover, calcium ions are effectively adsorbed on (211) crystal facets, thus prismatic and scalenohedral PCC crystals exhibit better adsorption performance than irregular cubic PCC ones. In addition, XRD demonstrates that PCC reduces or even eliminates the formation of crystals such as vaterite, displ... [more]

Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations’ energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station’s performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional−in... [more]