Yonggang Li

• PhD
• Professor (Full) at Central South University

Online detection of CO2 emissions can provide important data support for low-carbon operation of process industries. However, the cumbersome calibration process often limits the applications of gas sensors in field measurement. This article develops a total calibration-free gas detection method based on natural logarithm and linear convolution algo...

The multiple-reactor cascade operation is a distinctive characteristic in the process industry. However, it is difficult to establish an accurate and global model for multi-reactor cascading processes. Moreover, the intricate and dynamic operating state of the reactor, coupled with rear reactors, poses significant challenges to the fine control of...

Systematical measurement errors in industrial sensors should be avoided or rectified, as severe discrepancies in data can impede control, operation, and evaluation. However, some systematical errors are hard to estimate or experiment with. These errors often arise due to the change in external conditions. This means the measurement errors can be ig...

Cadmium is a typical by-product of zinc hydrometallurgy, which can compromise product quality and have significant environmental impacts if not properly handled. It has always been a challenge to find effective methods to measure its concentration due to the complex baseline and strong nonlinearity resulting from the background of high and varying...

The process industry is a key manufacturing process that consumes a vast amount of energy consumption. On the premise of ensuring process stability, controlling process variables to operate the process close to the optimal working condition plays a critical role in reducing energy consumption. Reinforcement learning (RL), using trial and error to l...

Complex industrial processes present typical uncertainty due to fluctuations in the composition of raw materials and frequently changing operating conditions. This poses three challenges for precise fault diagnosis, including random noise interference, less distinguishability between multi-class faults, and the new fault emerging. To address these...

Tunable diode laser absorption spectroscopy (TDLAS) is widely used for gas concentration measurements due to its merits of rapid, noncontact, and high-precision detection. Precise control of laser emission can guarantee the accuracy of the absorption spectrum at a specific wavelength. However, in an unstable environment on a real-world pharmaceutic...

Process industry indicator describes the production status and is crucial to the stable process operation. Its low sampling frequency makes it difficult to meet the indicator perception needs for real-time process control. Indicator estimation is a promising alternative to improve its obtaining frequency. However, the low sampling frequency of indi...

The roasting temperature is critical for enhancing product quality, reducing air pollution, and ensuring the long term operation of the zinc roasting process. However, optimizing the roasting temperature is challenging due to complex reaction mechanisms, feed composition fluctuations, and the coupling relationship with downstream processes. In this...

Precise control of roasting temperature is paramount for optimizing production efficiency in the zinc smelting process. However, existing research mainly focuses on average temperature control, and there is little research on temperature distribution control. To achieve this, a roasting temperature distribution model is first established based on t...

Full-spectrum detection (FSD) is widely used in wastewater treatment processes (WWTPs) due to its higher resistance to interference compared to single-wavelength detection. However, acquiring full-spectrum typically involves precision and expensive equipment, which significantly raises detection costs. This article proposes a low-cost full-spectrum...

Modern industry processes are typically composed of multiple operating units with reaction interaction and energy–mass coupling, which result in a mixed time-varying and spatial–temporal coupling of process variables. It is challenging to develop a comprehensive and precise fault detection model for the multiple interconnected units by simple super...

The electrowinning process is a critical operation in nonferrous hydrometallurgy and consumes large quantities of power consumption. Current efficiency is an important process index related to power consumption, and it is vital to operate the electrolyte temperature close to the optimum point to ensure high current efficiency. However, the optimal...

Roaster furnace is a large-scale equipment in zinc smelting process, which plays an significant role in safety production and enviromental protection. An accurate digital twin of roaster furnace can help to explore the influence of control parameters and serve as a test platform to verify the effectiveness of control strategies. However, the tradit...

Ensuring long-term safe and efficient operation of industrial processes relies on real-time identification of abnormal operating conditions. However, industrial processes frequently switch among diverse operating conditions and face harsh production environments. As a result, some extreme cases exist in historical abnormal samples can mask some sli...

The timely and accurate measurement of temperature field is of great significance for the low-carbon and high-efficiency operation of the zinc oxide rotary volatile kiln (ZORVK). Due to the large axial length, closed internal space and complex reaction mechanism, it is difficult to measure complete temperature field data. In this study, a novel tem...

The outstanding spectral resolution of full-spectrum detection (FSD) ensures excellent performance for complex scenarios in which target signals are affected by particle interference. However, FSD is limited by the cost of its measurement devices and demanding install requirements. Therefore, it is not always practical to utilize FSD for online wat...

Wavelength modulation-based tunable diode laser absorption spectroscopy (TDLAS/WMS) is widely used in various manufacturing processes. On the aseptic preparation filling production line, the seal quality of pharmaceutical glass vials should be strictly inspected in situ, where the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="h...

In recent decades, spectral analysis has become a key research field to determine product components. Ion concentrations in metallurgical liquid are crucial component parameters for guiding the stable process operation in zinc hydrometallurgy. Its rapid and accurate analysis plays a critical role in industrial informatization. However, on the one h...

To address the typical issue of insufficient labeled samples in the process of real time total nitrogen (TN) detection, a low-dimensional space-based redundancy minimization semi-supervised learning (LDSRM) framework is proposed. Unlike other graph-based semi-supervised algorithms, the proposed method performs semi-supervised modeling in a low-dime...

Online and accurate estimation of key performance indicators (KPI) is the foundation for operational optimization of a chemical process. However, a chemical process usually consists of multiple reactors, and the factors influencing KPI are spatially distributed in the long process flow. In addition, due to the distinct time lags between KPI and eac...

In practical TN detection, inaccurate labels, sample noises, and the insufficiency of labeled samples are the three most predominant problems which degrade the detection performance. In this paper, we proposed a novel anti-noise semi-supervised learning method called multi-regularized robustness semi-supervised learning (MR²S²L), which can establis...

Accurately predicting the short- and long-term variations of total nitrogen (TN) is vital for operating the wastewater treatment plants (WWTPs), considering the critical role TN plays in reflecting the eutrophication of wastewater. However, only a few relevant water quality parameters with limited samples can be obtained in WWTPs, which tremendousl...

Soft sensor plays a progressively significant role in modern industrial processes. However, process variables usually have complex distribution characteristics, which can adversely affect the performance of soft sensor. On the other hand, due to the inevitable presence of noise in industrial data, the effect of traditional prediction models based o...

The main task of the impurity removal process is to control the oxidation reduction potential (ORP) within the range of the optimized set value. The impurity removal process is essentially an oxidation-reduction process. Oxidation reduction potential (ORP) is an external reflection of reaction state inside the impurity removal reactor. However, act...

In the process of electrocoagulation purification, there is a problem of passivation of the reactor electrode plates, and the process operating state is constantly changing, which badly affects the efficiency of electrocoagulation purification and the removal of heavy metal ion. However, in order to ensure that the concentration of heavy metal ion...

With the increasingly stringent environmental protection policies of various countries, the contradiction between the treatment cost and the purification degree of environmental pollutants has become increasingly significant, which has become a major factor restricting the efficient operation of wastewater treatment plants. Hence, keeping the ion c...

Parameters monitoring is essential to maintain the stability and efficiency of the wastewater treatment process, which has spurred ubiquitous installation of sensors in wastewater treatment plants (WWTPs). As the rich process data of WWTPs is not effectively transformed into actionable knowledge for system optimization due to improper sensor instal...

To determine the total iron concentration in aqueous solutions containing complexing matrix, a direct and rapid ultraviolet-visible spectroscopy determination method was developed using the Nitroso R salt as a chromogenic reagent. In the presence of NaI and CTMAB, the experiment shows that Nitroso R salt-Fe complex can produce a significantly sensi...

Nonferrous metal industry is the foundation of China's substantial economy and plays a key role in national economy and defense construction. Industrial software is crucial for the high-quality development of the nonferrous metal industry and is associated with the in-depth implementation of national software development strategies. Currently, the...

Deep learning based soft analyzers are important for modern industrial process monitoring and measurement, which aim to establish prediction model between quality data and easy-to-measure variables. However, in traditional deep learning methods, the guidance of quality information on feature extraction is insufficient and easily reduces with the in...

Discrete and delayed laboratory analyses of product quality restrict the operational optimization of industrial processes. However, it is challenging to build an accurate online estimation model for product quality because of complex process dynamics, multiple working conditions and multi-rate characteristics. Therefore, a multimode mechanism-guide...

Timely and accurate detection of abnormal working conditions can ensure stability, improve production efficiency and reduce pollution of an industrial process. However, the production data of an industrial process has non-Gaussian and time-varying characteristics due to the diverse feed composition and complex reaction mechanisms. To address the ab...

In the neutral leaching process of zinc hydrometallurgy, exploring the characteristics of pH fluctuations in the reactor is an effective approach for improving the zinc leaching rate. A modeling method of the pH probability density distribution based on the Gaussian mixture model (GMM) has thus been proposed to describe the characteristics of pH fl...

Rapidly and accurately detect the total nitrogen (TN) concentration is enormously important for surface water protection considering the critical role it plays in reflecting the eutrophication of surface water. However, traditional TN detection methods have to experience a tedious oxygen digestion process, which tremendously limits the detection sp...

Oxygen invasion into pharmaceutical glass vials directly affects the quality of medicine, and noncontact inversion of the residual oxygen concentration can effectively determine whether the encapsulated medicine is qualified. Different from studies in an airtight chamber, it is impractical to build a linear inversion model of an open-path optical e...

Industrial Internet-of-Things (IIoT) are highly vulnerable to cyber-attacks due to their open deployment in unattended environments. Intrusion detection is an efficient solution to improve security. However, because the labeled samples are difficult to obtain, and the sample categories are imbalanced in real applications, it is difficult to obtain...

To solve the problem of insufficient labeled samples in online total nitrogen (TN) detection, a novel semisupervised learning (SSL) method called double regularized structure graph learning (DRSGL) was proposed, which can effectively extract useful features and support the TN detection equipment to establish accurate detection models with few label...

This paper investigates the nonuniform and nonconvex input constrained flocking control problem of continuous-time multi-agent systems. A distributed flocking control algorithm is proposed for each agent using the local information from its neighbor agents subject to nonuniform and nonconvex control constraints. Based on a constraint scaling factor...

The suspended micro particles (SMP) in the Zinc leaching liquid will cause the scattering of detection light, which will make the spectral data of the liquid rise nonlinearly, leading to the difficulty of accurate detection of ion concentration by ultraviolet-visible spectroscopy (UV-Vis). Therefore, based on UV-Vis, a new ion concentration detecti...

In situ and accurate oxygen concentration detection in pharmaceutical glass vials is of great significance in the development of the pharmaceutical industry. This paper reveals a universal data distribution rule in which each sampling point follows an approximately normal distribution with its own mean (μ) and standard deviation (σ) in a wavelength...

In this paper, the rotating consensus problem for the multi-agent systems of double-integrator dynamic is mainly considered with and without communication delay. A fully distributed control strategy is given on the more general complex plane. For the case without communication delay, we design a distributed control protocol with the help of local r...

This paper is concerned with the problem of short circuit detection in infrared image for metal electrorefining with an improved Faster Region-based Convolutional Neural Network (Faster R-CNN). To address the problem of insufficient label data, a framework for automatically generating labeled infrared images is proposed. After discussing factors th...

Roasting is the first procedure in the zinc smelting process. The stable and safe operation of roasting process is significant to guarantee the quality of output zinc and reduce industrial pollution and energy consumption. In order to realize safe and stable operation for the roasting process, it is particularly important to monitor the roasting pr...

With the development of sensor and communication technology, industrial systems have accumulated a large amount of data. This data has provided new perspectives and methods for industrial system analysis, monitoring and control, which is proven to be of great significance. However, with the collection and storage of industrial data in a 7 × 24 mann...

Soft sensors have been extensively applied for predicting difficult-to-measure quality variables. However, industrial processes are often characterized with the nonlinearity and time variance, which makes it difficult to accurately predict the quality variables. In this paper, a just-in-time learning (JITL) based mixed kernel principal component we...

In most of industrial processes, there are mainly two issues: 1. working models will be different at different time (multi-models); 2. different variables have different sampling periods (dual-sampling periods), in which the sampling period of the difficult-to-measure variable is larger than the easy-to-measure variable in most cases. Typically, th...

Visual titration is an important method for detecting the main and minor components in the samples. In visual titration, the image features are easily affected by the dichroism of the indicator, ambient light, and bubbles in the solution, which adversely effect the precision of titration endpoint recognition. This paper proposes a similarity method...

The multi-sensor data fusion based data-driven fault diagnosis method is a promising approach to detect faults of complex systems. However, in the actual industrial environment, the sampling rate of different sensors is often inconsistent. In order to apply this kind of data to fault diagnosis, the traditional methods are to preprocess it and conve...

Data-driven process-monitoring methods have been the mainstream for complex industrial systems due to their universality and the reduced need for reaction mechanisms and first-principles knowledge. However, most data-driven process-monitoring methods assume that historical training data and online testing data follow the same distribution. In fact,...

In the process of electrochemical wastewater treatment, the removal rate of electrocoagulation reactor will be affected by various factors such as the pH value of wastewater solution, the current density, the wastewater flow rate and the initial concentration of heavy metal ions. Therefore, this study proposes a prediction method of the removal rat...

With the development of information and communication technologies, industrial cyber-physical systems (ICPSs) have accumulated a large amount of data, which enables us to convert data into industrial insight. However, since the industrial process of ICPS is always complicated and large-scale, the raw data only contains a few operation condition inf...

In visible and near-infrared spectroscopic analysis, the noise signals are unavoidable and seriously affect the accuracy and precision of measurement. Considering the characteristics of different components in the mixed spectrum, we propose a least angle regression assessment algorithm based on joint dictionary to remove the noise in this paper. Fi...

Time-delay estimation is an important step for soft sensor modeling. In practical industrial process, the transportation time of materials and the transmission time of signals are fluctuating, and the traditional static time-delay estimation (STDE) methods can no longer accurately extract the dynamic time-delay characteristics. In this paper, a wei...

As one of the most important preprocessing procedures in spectral detection, wavelength selection approaches play an irreplaceable role in reducing the model overfitting and prediction errors. In this paper, we propose a two-step wavelength selection method called interval permutation combination population analysis (iPCPA), which improves the sele...

In order to ensure the long-term stable operation of a large-scale industrial process, it is necessary to detect and solve the minor abnormal conditions in time. However, the large-scale industrial process contains a large number of complex related process variables, some of which are redundant for abnormal condition detection. To solve this proble...