Xin Peng

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Verified

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• Doctor of Philosophy
• Professor at East China University of Science and Technology

This paper proposes a variable-gain state observer-based sliding mode self-healing control (VSO-based SMSHC) method for a wastewater treatment process (WWTP) with changeable external disturbances and sensor failures. To reconstruct the disturbances and unmeasured system states, a novel VSO is developed, in which the VSO gains are adjusted by follow...

Under closed-set scenarios (CSS), distributed modeling performs well in fault diagnosis of plant-wide industrial processes due to its flexibility and robustness. However, a more realistic scenario is often open, where unseen situations may arise unexpectedly, rendering existing methods infeasible. The advent of open-set recognition algorithms that...

Forecasting NO $_x$ concentration in fluid catalytic cracking (FCC) regeneration flue gas can guide the real-time adjustment of treatment devices, and then furtherly prevent the excessive emission of pollutants. The process monitoring variables, which are usually high-dimensional time series, can provide valuable information for prediction. Althou...

Data-driven process monitoring methods are widely used in industrial tasks, with visual monitoring enabling operators to intuitively understand operational status, which is vital for maximizing industrial safety and production efficiency. However, high-dimensional industrial data often exhibit complex structures, making the traditional 2D visualiza...

Considering the complexity of centralized plant-wide optimization and the presence of communication delay in production units, a distributed asynchronous optimization framework is developed for energy consumption optimization problem during ethylene production. First, the energy consumption optimization problem of ethylene process is formulated int...

With the widespread integration of distributed renewable energy, traditional centralized dispatching architectures face challenges in robustness and flexibility. It is essential to develop distributed energy optimization strategies to enhance the efficient utilization of renewable energy. Nevertheless, the exchange of information in distributed dis...

Energy trading in industrial parks has great potential for reducing carbon emissions and lowering energy bills. This article proposes a safe multi-agent deep reinforcement learning algorithm for optimizing the energy trading strategy in industrial parks to achieve less reliance on the main grid and save energy costs. Specifically, an industrial par...

Gas leakage can lead to catastrophic consequences on both the environment and human health. To mitigate these losses, it is imperative to develop accurate and efficient spatiotemporal models for gas dispersion. The gas diffusion process occurs in a 3-dimensional (3D) space, but most research has been confined to flat-plane scenarios, neglecting the...

Privacy-preserving consensus can address the information being leaked during distributed computing, encouraging its application in various scenarios. This paper investigates the finite-time privacy-preserving distributed optimal dispatch for energy systems (ESs). Firstly, a dynamic output mask function is designed to ensure that each node's interna...

In the wastewater treatment process, unsupervised domain adaptation enables cross-condition prediction for key performance indicators. However, the lack of interpretability in predictions can compromise the reliability of the model. This work proposes a transferable ensemble additive network (TEAN) that is both capable of solving domain adaptation...

As environmental pollution becomes increasingly serious and industrial energy consumption continuously rises, an intelligent and efficient industrial energy management policy is urgently needed to reduce costs and maximize the benefits of industrial energy systems. However, modern industrial energy systems are characterized by hybrid industrial equ...

Anaerobic granular sludge (AnGS) activity measurement has been a hot research topic in the field of wastewater treatment. The traditional method of evaluating activity by biochemical reaction is time-consuming, and in recent years, the method based on image color modeling is faster but less accurate. This paper summarizes the shortcomings of the ex...

Meter pointers exhibit stable and anti-interference capabilities, rendering them extensively utilized in industrial environments. However, automated reading poses a significant challenge due to the fact that current segmentation methods struggle to isolate the fine-grained pointers and scales for accurate reading calculations. This challenge can be...

X-ray diffraction (XRD) is used for characterizing the crystal structure of molecular sieves after synthetic experiments. However, for a high-throughput molecular sieve synthetic system, the huge amount of data derived from large throughput capacity makes it difficult to analyze timely. While the kernel step of XRD analysis is to search peaks, an a...

Data-driven methods for predicting quality variables in wastewater treatment processes (WWTPs) have mostly ignored the slow time-varying nature of WWTP, and they are data-consuming that need a large amount of independent and homogeneously distributed data, which makes it difficult to collect. To address this issue with few-shot and inconsistent dis...

This paper considers the secure distributed control consensus problem of linear multi‐agent systems (MASs) under switching topologies, subject to intermittent sequential scaling attacks, which was compelled to scaling factor, attack frequency, duration and cooperative‐competitive networks. First, the scenario of a fixed topology is considered, and...

This article focuses on fault detection for nonlinear industrial processes with multiple operating conditions, in which transfer learning is used to deal with the limited training data issue for unusual operating conditions. To this end, the Tucker decomposition is first implemented to deliver the Gaussian kernel of the nonlinear processes with mul...

In wastewater treatment processes, building performance evaluation models to predict key indicators under uncommon operating conditions is difficult due to the lack of labeled data. Domain adaptation can be used to solve this problem through leveraging the knowledge of common conditions to construct prediction models for uncommon conditions. Consid...

In this work, a new synchronous adjustment framework for the integrated hydrogen network based on multi-model ensemble method is proposed. Specifically, the contributions of this work can be highlighted as follows: ⋅ A model constructed for describing the synergistic relationship between the integrated hydrogen network is proposed. Compared with th...

In real-world fault diagnosis tasks, it is never possible to enumerate all fault types beforehand since there are always unseen situations that may arise unexpectedly. Therefore, fault diagnosis is essentially an open-world recognition task. A desirable open-world fault diagnosis model must be able to perform open-set recognition (OSR) and incremen...

The presence of outliers in the training data affects the accuracy of the constructed model. To cope with the outlier interference in the model construction process, some robust methods have been proposed on the basis of the nonparametric method, Gaussian process regression, without eliminating the outliers previously. However, the high complexity...

With the increase of wastewater treatment volume and the insufficiency of preventive maintenance measures, the potential safety hazards of wastewater treatment process (WWTP) are gradually emerging. The unplanned sensor failures may result in false information feedback and reduce the reliability of the control system. This paper attempts to formula...

This paper is concerned with the optimal fault detection issues for discrete-time nonlinear systems with the aid of Takagi-Sugeno (T-S) fuzzy dynamic modelling technique. To this end, in the first part of this paper, the nonlinear system is formulated in the time-varying fuzzy manner, and based on it, a unified fault detection approach is developed...

Fault detection in the wastewater treatment process (WWTP) has been well-addressed when the distributions of training data (source domain) and testing data (target domain) are consistent. However, the distributions may be inconsistent in actual processes due to the variable working conditions caused by the fluctuations in the external environment....

Fault diagnosis of industrial processes plays an important role in avoiding heavy losses and ensuring production safety. Complex industrial processes often have many working conditions, and the actual industrial process often concentrates on certain working conditions. As a result, the running time of some working conditions is shorter, so the data...

The online optimization of gasoline blending benefits refinery economies. However, the nonlinear blending mechanism, the oil property fluctuations, and the blending model mismatch bring difficulties to the optimization. To solve the above issues, this paper proposes a novel online optimization method based on deep reinforcement learning algorithm (...

In order to improve the utilization rate of coal generation and reduce carbon emissions from coal-fired boilers, the operation parameters of power plant boilers should be matched with the actual burning coal. Due to the complex and high-risk blending process of multiple coal types, the actual application of coal types inconsistent with expectations...

A desirable open world recognition (OWR) system requires performing three tasks: (1) Open set recognition (OSR), i.e., classifying the known (classes seen during training) and rejecting the unknown (unseen$/$novel classes) online; (2) Grouping and labeling these unknown as novel known classes; (3) Incremental learning (IL), i.e., continual learning...

This study focuses on building an intelligent decision-making attention mechanism in which the channel relationship and conduct feature maps among specific deep Dense ConvNet blocks are connected to each other. Thus, develop a novel freezing network with a pyramid spatial channel attention mechanism (FPSC-Net) in deep modeling. This model studies h...

Scanning electron microscope (SEM) images generated in the experiment of high-throughput molecular sieves catalysts contain particle size information of various molecular sieves. However, a certain proportion of molecular sieves in SEM images are occluded due to their characteristics of cluster distribution, which cause difficulties to measure the...

Cluster assignment of large and complex datasets is a crucial but challenging task in pattern recognition and computer vision. In this study, we explore the possibility of employing fuzzy clustering in a deep neural network framework. Thus, we present a novel evolutionary unsupervised learning representation model with iterative optimization. It im...

Intelligent fault diagnosis method is an important tool for ensuring the stability of industrial processes. However, in the actual industrial process, forming a fault diagnosis model with good performance is difficult because of the complexity of feature extraction and the lack of labelled fault data. Data enhancement on the basis of the original d...

In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud computing and big data storage greatly fac...

Dear Editor, This letter is concerned with the data-driven fault compensation tracking control for a coupled wastewater treatment process (WWTP) subject to sensor faults. Invariant set theory is introduced to eliminate the completely bounded and differentiable conditions of coupled non-affine dynamics and to explicitly express the control inputs. A...

Near-Infrared (NIR) spectroscopy has become an important analytical tool to perform rapid characterization of petroleum products due to its effectiveness and efficiency. However, NIR analysis is a typical small sample problem and unitary regression model under specific assumption often suffers from poor generalization ability during online implemen...

Accurate segmentation of human tissue structure from medical images is one of the critical links in medical image diagnosis. However, due to the medical image scale of different tissues varying significantly and being structurally complex, the low contrast between tissues and background in some medical imaging makes it challenging to identify. The...

The increasing utilization of wastewater necessitates dedicated attentions to the potential security threats, and formulate strategies for defense, response, and future protection. The nonideal actuator subject to the faults and constraints may underload the driving force and reduce the sewage purification efficiency. This article proposes an adapt...

To solve the anti-disturbance control problem of dissolved oxygen concentration in the wastewater treatment plant (WWTP), an anti-disturbance control scheme based on reinforcement learning (RL) is proposed. An extended state observer (ESO) based on the Takagi–Sugeno (T-S) fuzzy model is first designed to estimate the the system state and total dist...

This brief deals with the secure bipartite consensus problem for linear multi-agent systems (MASs) with Denial-of-Service (DoS) attacks subject to an observer-based dynamic event-triggered strategy (OBDES). Two event-triggered strategies involved with dynamic threshold are developed in controller and observer channels. Based on the connected struct...

Source term estimation (STE) is crucial for understanding and addressing hazardous gas leakages in the chemical industry. Most existing methods basically use an atmospheric transport and dispersion (ATD) model to predict the concentrations of hazardous gas leakages from different possible sources, compare the predicted results with multi-sensor dat...

This paper investigates the performance-guaranteed adaptive self-healing control for wastewater treatment processes (WWTPs), in which the non-ideal actuator, i.e., the actuator suffering from faults and constraints is considered. Firstly, an error conversion dynamic model based on a new prespecified-time performance function is presented to guarant...

Hazardous gas leakage can cause irreversible damage to the environment and human health. When it happens, it's necessary to find the accurate position of the leaking source efficiently and take effective measures to reduce or prevent more irreversible losses. However, source tracking in the scenario with complex obstacles faces the challenge caused...

In soft sensing, quality indicators are predicted by the signals of the discrete process variables in the time domain, in which time delay exists in the process variables and quality indicators. Traditionally, discrete variables are used to estimate quality indicators directly through the time-series model, in which the sliding window is used to ex...

In a large-scale industrial system with numerous variables, the relations among variables are often nonlinear and very complicated, due to material, energy and information flows throughout the entire system. In such systems, fault detection and diagnosis (FDD) suffer from the strong interference of fault-free variables and hence become more difficu...

Due to environmental fluctuations, the operating performance of complex industrial processes may deteriorate and affect economic benefits. In order to obtain maximal economic benefits, operating performance assessment is a novel focus. Therefore, this paper proposes a whole framework from operating performance assessment to nonoptimal cause identif...

Soft sensors estimate quality indicators that are difficult to measure online so that they are important in industrial processes. The sensors may malfunction so that some data may be unavailable or contain abnormal values, which means the data contain missing values. To deal with missing values, missing values are filled by evaluated estimations or...

Supervised representation learning based on the teacher-student framework can extract quality-related features for soft sensors, in which the teacher network extracts representation information for the student network as supervision information. In traditional applications, the teacher network is heavy and is difficult to train, so the teacher netw...

Plant-wide optimization plays a vital role in improving the overall performance of large-scale industrial processes. Considering the modeling complexity and convergence difficulty of centralized plant-wide optimization, this paper proposes a distributed framework by decomposing the global optimization problem into a set of subproblems, where multip...

In the above article [1] , row 3 paragraph 3 in Section III-B, the width $w$ equals to $x_{2}-x_{1}$ as it is shown in Fig. 4.