Hybrid Soft Sensor Research Articles

Modelling the influence of tool wear on shape errors in milling using a hybrid soft-sensor

AbstractThe wear of cutting tools during milling processes not only constrains the volume of material that can be removed by a tool but also results in a progressive deterioration of the quality of the workpiece. Although there are established methodologies for predicting tool wear, there is a paucity of knowledge regarding the impact of tool wear on shape error. The authors present a data-driven soft sensor to model the effect of tool wear on shape error, obviating the need for direct tool wear measurement. To evaluate this approach, a milling experiment was conducted, wherein process forces, spindle current, and resulting shape error were measured. Furthermore, a geometrical cutting simulation was conducted in order to obtain cutting conditions, including the volume of material removed. This study examines the contribution of these features to the prediction performance of the proposed soft sensor. Additionally, the transferability from models trained on different tools is investigated to ascertain the impact of tool wear variance on prediction performance. Prediction experiments demonstrate that a soft-sensor based on a combination of simulation and process monitoring data enables a model trained on data from multiple milling tools to account for wear and predict shape error well under varying wear scenarios. The approach presented here has been demonstrated to result in a reduction of the prediction error of up to 60% compared to an average baseline prediction.

A Hybrid Soft Sensor Model for Measuring the Oxygen Content in Boiler Flue Gas.

As an indispensable component of coal-fired power plants, boilers play a crucial role in converting water into high-pressure steam. The oxygen content in the flue gas is a crucial indicator, which indicates the state of combustion within the boiler. The oxygen content not only affects the thermal efficiency of the boiler and the energy utilization of the generator unit, but also has adverse impacts on the environment. Therefore, accurate measurement of the flue gas's oxygen content is of paramount importance in enhancing the energy utilization efficiency of coal-fired power plants and reducing the emissions of waste gas and pollutants. This study proposes a prediction model for the oxygen content in the flue gas that combines the whale optimization algorithm (WOA) and long short-term memory (LSTM) networks. Among them, the whale optimization algorithm (WOA) was used to optimize the learning rate, the number of hidden layers, and the regularization coefficients of the long short-term memory (LSTM). The data used in this study were obtained from a 350 MW power generation unit in a coal-fired power plant to validate the practicality and effectiveness of the proposed hybrid model. The simulation results demonstrated that the whale optimization algorithm-long short-term memory (WOA-LSTM) model achieved an MAE of 0.16493, an RMSE of 0.12712, an MAPE of 2.2254%, and an R2 value of 0.98664. The whale optimization algorithm-long short-term memory (WOA-LSTM) model demonstrated enhancements in accuracy compared with the least squares support vector machine (LSSVM), long short-term memory (LSTM), particle swarm optimization-least squares support vector machine (PSO-LSSVM), and particle swarm optimization-long short-term memory (PSO-LSTM), with improvements of 4.93%, 4.03%, 1.35%, and 0.49%, respectively. These results indicated that the proposed soft sensor model exhibited more accurate performance, which can meet practical requirements of coal-fired power plants.

Bringing a scalable adaptive hybrid modeling framework closer to industrial use: Application on a multiscale fungal fermentation.

Digitalization has paved the way for new paradigms such as digital shadows and digital twins for fermentation processes, opening the door for real-time process monitoring, control, and optimization. With a digital shadow, real-time model adaptation to accommodate complex metabolic phenomena such as metabolic shifts of a process can be monitored. Despite the many benefits of digitalization, the potential has not been fully reached in the industry. This study investigates the development of a digital shadow for a very complex fungal fermentation process in terms of microbial physiology and fermentation operation on pilot-scale at Novonesis and the challenges thereof. The process has historically been difficult to optimize and control due to a lack of offline measurements and an absence of biomass measurements. Pilot-scale and lab-scale fermentations were conducted for model development and validation. With all available pilot-scale data, a data-driven soft sensor was developed to estimate the main substrate concentration (glucose) with anormalized root mean squared error(N-RMSE)of 2%. This robust data-driven soft sensor was able to estimate accurately in lab-scale (volume < 20× pilot) with a N-RMSE of 7.8%. A hybrid soft sensor was developed by combining the data-driven soft sensor with a mass balance to estimate the glycerol and biomass concentrations on pilot-scale data with N-RMSEs of 11% and 21%, respectively. A digital shadow modeling framework was developed by coupling a mechanistic model (MM) with the hybrid soft sensor. The digital shadow modeling framework significantly improved the predictability compared with the MM. The contribution of this study brings the application of digital shadows closer to industrial implementation. It demonstrates the high potential of using this type of modeling framework for scale-up and leads the way to a new generation of in silico-based process development.

A hybrid soft sensor for key product yield of FCC unit based on deep learning framework driven by data and process mechanism

The model for online prediction of product yield embedded in the optimization and control framework has the potential to improve the profits of fluid catalytic cracking (FCC) units. In this work, a hybrid soft sensor model is developed to predict key product yield in commercial FCC units. The deep learning framework based on semi-supervised learning is driven by data and process mechanism to construct the hybrid model with a double-layer structure. Moreover, process simulation strategy is utilized for key input variable selection of the hybrid model to ensure model reliability. The hybrid model is compared to a pure data-driven model in terms of accuracy and trend consistency of model predictions. The results demonstrate that the hybrid model exhibits a superior prediction performance. This work suggests that the hybrid model can drive innovation and industrial practice in the optimization and control of FCC units.

Modeling an Adaptive Hybrid Soft Sensor with Co-training Learning toward Applications in Wastewater Treatment

Modeling an Adaptive Hybrid Soft Sensor with Co-training Learning toward Applications in Wastewater Treatment

An evolutionary deep learning soft sensor model based on random forest feature selection technique for penicillin fermentation process

Accurate and reliable measurement of key biological parameters during penicillin fermentation is of great significance for improving penicillin production. In this research context, a new hybrid soft sensor model method based on RF-IHHO-LSTM (random forest-improved​ Harris hawks optimization-long short-term memory) is proposed for penicillin fermentation processes. Firstly, random forest (RF) is used for feature selection of the auxiliary variables for penicillin. Next, improvements are made for the Harris hawks optimization (HHO) algorithm, including using elite opposition-based learning strategy (EOBL) in initialization to enhance the population diversity, and using golden sine algorithm (Gold-SA) in the search strategy to make the algorithm accelerate convergence. Then the long short-term memory (LSTM) network is constructed to build a soft sensor model of penicillin fermentation processes. Finally, the hybrid soft sensor model is used to the Pensim platform in simulation experimental research. The simulation test results show that the established soft sensor model, with high accuracy of measurement and good effect, can meet the actual requirements of engineering.

Mechanism Analysis and Self-Adaptive RBFNN Based Hybrid Soft Sensor Model in Energy Production Process: A Case Study.

Despite hard sensors can be easily used in various condition monitoring of energy production process, soft sensors are confined to some specific scenarios due to difficulty installation requirements and complex work conditions. However, industrial process may refer to complex control and operation, the extraction of relevant information from abundant sensors data may be challenging, and description of complicated process data patterns is also becoming a hot topic in soft-sensor development. In this paper, a hybrid soft sensor model based mechanism analysis and data-driven is proposed, and ventilation sensing of coal mill in a power plant is conducted as a case study. Firstly, mechanism model of ventilation is established via mass and energy conservation law, and object-relevant features are identified as the inputs of data-driven method. Secondly, radial basis function neural network (RBFNN) is used for soft sensor modeling, and genetic algorithm (GA) is adopted for quick and accurate determination of the RBFNN hyper-parameters, thus self-adaptive RBFNN (SA-RBFNN) is proposed to improve the soft sensor performance in energy production process. Finally, effectiveness of the proposed method is verified on a real-world power plant dataset, taking coal mill ventilation soft sensing as a case study.

Estimating Phosphorus and COD Concentrations Using a Hybrid Soft Sensor: A Case Study in a Norwegian Municipal Wastewater Treatment Plant

Online monitoring of wastewater quality parameters is vital for an efficient and stable operation of wastewater treatment plants (WWTP). Several WWTPs rely on daily/weekly analysis of water samples rather than online automated wet-analyzers due to their high capital and maintenance costs. Soft-sensors are emerging as a viable alternative for real-time monitoring of parameters that either lack a reliable measuring principle or are measured using expensive online sensors. This paper presents the development, implementation, and validation of a hybrid soft sensor used to estimate Total Phosphorus (TP) and Chemical Oxygen Demand (COD) in the influent and effluent streams of a full-scale WWTP. A systematic method for cleaning and processing sensor data, identifying statistically significant correlations, and developing a mathematical model, is discussed. A non-intrusive Industrial Internet of Things (IIoT) infrastructure for soft-sensor deployment and a web-based GUI for data visualization are also presented in this work. The values of TP and COD estimated by the soft sensor are validated by comparing the estimated values to the daily average of their corresponding lab measurements. The data validation results demonstrate the potential of soft sensors in providing real-time values of essential wastewater quality parameters with an acceptable degree of accuracy.

A Low Discrepancy Heuristic Evolution ELM and Ground Effect Theory-Based Serial Hybrid Soft Sensor Model of Floating Height in Air Cushion Furnace

In air cushion furnace, the floating height is a key process parameter which greatly affects the quality and production efficiency of high quality metal strips. However, the floating height is hard to be collected in the complex and abominable industry environment. Furthermore, due to the flow field characteristics, some important process variables are difficult to accurately calculate by traditional mechanism modeling methods. In order to accurately predict the floating height, firstly, a low discrepancy heuristic evolution ELM and ground effect theory based serial hybrid soft sensor model is proposed, which constituted by a mechanism model and two data driven models. Secondly, based on the force equilibrium equation and ground effect theory, the mechanism model is constructed, which describes the relationship between the floating height and the process variables including the jet impact angle. Thirdly, a low discrepancy heuristic evolution ELM is proposed as the data driven model to predict the jet impinging angle. In the data driven model, the novel dual mutation strategies collaboration differential evolution is proposed to guarantee the low discrepancy and physical applicability of data driven model. The effectiveness of the proposed method was validated on the self-developed air cushion experiment platform and got desirable experimental results. The research lays an important foundation for the successful implementation of monitoring and control of the strip floating process.

Moisture soft sensor for agitated pan dryers using a hybrid modeling approach.

A hybrid soft sensor model is developed to provide real-time estimations of the moisture content in the Active Pharmaceutical Ingredient (API) wet cake for an agitated pan drying process. The soft sensor is calibrated using data from 5 batches. The estimation accuracy of the soft sensor is demonstrated with additional 21 commercial scale batches. Dynamic global sensitivity analysis has been performed to investigate the significance of input process parameters on the variability of soft sensor estimations over the whole drying process. The results indicate that the soft sensor acts as a powerful tool for real-time estimation of the moisture content, which can consequently be used to monitor and control the API drying process and enable Quality by Design (QbD) approach for this important pharmaceutical process.

Adaptive Hybrid Soft-Sensor Model of Grinding Process Based on Regularized Extreme Learning Machine and Least Squares Support Vector Machine Optimized by Golden Sine Harris Hawk Optimization Algorithm

Soft-sensor technology plays a vital role in tracking and monitoring the key production indicators of the grinding and classifying process. Least squares support vector machine (LSSVM), as a soft-sensor model with strong generalization ability, can be used to predict key production indicators in complex grinding processes. The traditional crossvalidation method cannot obtain the ideal structure parameters of LSSVM. In order to improve the prediction accuracy of LSSVM, a golden sine Harris Hawk optimization (GSHHO) algorithm was proposed to optimize the structure parameters of LSSVM models with linear kernel, sigmoid kernel, polynomial kernel, and radial basis kernel, and the influences of GSHHO algorithm on the prediction accuracy under these LSSVM models were studied. In order to deal with the problem that the prediction accuracy of the model decreases due to changes of industrial status, this paper adopts moving window (MW) strategy to adaptively revise the LSSVM (MW-LSSVM), which greatly improves the prediction accuracy of the LSSVM. The prediction accuracy of the regularized extreme learning machine with MW strategy (MW-RELM) is higher than that of MW-LSSVM at some moments. Based on the training errors of LSSVM and RELM within the window, this paper proposes an adaptive hybrid soft-sensing model that switches between LSSVM and RELM. Compared with the previous MW-LSSVM, MW-neural network trained with extended Kalman filter(MW-KNN), and MW-RELM, the prediction accuracy of the hybrid model is further improved. Simulation results show that the proposed hybrid adaptive soft-sensor model has good generalization ability and prediction accuracy.

Towards a digital twin: a hybrid data‐driven and mechanistic digital shadow to forecast the evolution of lignocellulosic fermentation

AbstractThe high substrate variability and complexity of fermentation media derived from lignocellulosic feedstock affects the concentration profiles and the length of the fermentation. Failing to account for such variability raises operational and scheduling issues and affects the overall performance of these processes. In this work, a hybrid soft sensor was developed to monitor and forecast the evolution of cellulose‐to‐ethanol fermentation. The soft sensor consisted of two modules (a data‐driven model and a kinetic model) connected sequentially. The data‐driven module used a partial‐least‐squares model to estimate the current state of glucose from spectroscopic data. The kinetic model was recursively fitted to known concentrations of glucose to update the long‐horizon predictions of glucose, xylose, and ethanol. This combination of real‐time data update from an actual fermentation process to a high‐fidelity kinetic model constitutes the basis of the digital twin concept and allows for a better real‐time understanding of complex inhibition phenomena caused by different inhibitors commonly found in lignocellulosic feedstocks. The soft sensor was experimentally validated with three different cellulose‐to‐ethanol fermentations and the results suggested that this method is suitable for monitoring and forecasting fermentation when the measurements provide reasonably good estimates of the real state of the system. These results would allow the flexibility of the operation of cellulosic processes to be increased, and would permit the scheduling to be adapted to the inherent variability of such substrates © 2020 Society of Chemical Industry and John Wiley &amp; Sons, Ltd.

Hybrid soft sensor modeling for bisphenol‐A synthesis reaction process

AbstractTo online estimate the concentrations of key components at the outlet of bisphenol‐A (BPA) synthesis reactor, a hybrid modeling approach is proposed in this paper. First, the simplified mechanism model for the synthesis of BPA in an industrial fixed‐bed reactor is deduced on the basis of mass and energy balance. Second, considering that the initial concentrations of reactants at the inlet of BPA synthesis reactor cannot be measured in real‐time, the modified stochastic gradient boosting‐Gaussian process regression approach previously proposed by the authors is employed to online estimate the initial concentrations of reactants. Furthermore, modified stochastic gradient boosting‐Gaussian process regression method is also adopted to model the nonlinear influence of the reaction factors ( i.e., initial concentrations of reactants and inlet temperature) on the reaction rate constants in the simplified mechanism model. Finally, the hybrid modeling approach for BPA synthesis process is achieved by integrating the simplified mechanism model with the estimation models of initial concentrations and reaction rate constants. The feasibility and validity of the proposed hybrid model is verified through an industrial BPA synthesis process. On the basis of the hybrid model, the sensitivity analyses of the initial concentrations and inlet temperature are conducted, which can provide theoretical guidance for the operation optimization study in future work.

Process monitoring and fault detection on a hot-melt extrusion process using in-line Raman spectroscopy and a hybrid soft sensor

We propose a real-time process monitoring and fault detection scheme for a pharmaceutical hot-melt extrusion process producing Paracetamol-Affinisol extrudate. The scheme involves prediction of Paracetamol concentration from two independent sources: a hybrid soft sensor and a Raman-based Partial Least Squares (PLS) calibration model. Both these predictions are used by the developed PCA (Principal Component Analysis) and SPC (Statistical Process Control) monitors to detect process faults and raise alarms. Through real-time extrusion results, it is shown that this two-sensor approach enables the detection of various common process faults which would otherwise remain undetected with a single-sensor monitoring scheme.

Decentralized detection of hybrid faults in mobile sensor nodes

The widespread use of sensor nodes that are operating under the Internet of Things paradigm motivates researcher to step forward and build reliable systems capable of detecting their faulty nodes. These nodes lead to decrease in the accuracy and functionality of the networks, which finally result in quality degradation of network services. From the temporal point of view, the faults can be either permanent or intermittent. The detection of the latter one is more challenging since the nodes show contradictory behaviors at different times. from the topological point of view, the mobility of the sensor nodes is an intrinsic characteristic in many IoT-based applications, where numerous mobile nodes are managed by static overlay nodes. The dynamics of these network introduces the second challenge in identifying faulty nodes. Several works have been conducted to address the problem, but there is a research gap in identifying hybrid soft sensor faults in the aforementioned networks. The focus of attention in this paper is the detection of soft faults in the sensing unit of the nodes. We devised a new method, called Hybrid Fault Detection in Mobile Sensors, to detect nodes with mixed permanent and intermittent faults. A software debugging approach inspired the main idea. We also applied data mining techniques such as DBSCAN and K-means to validate sensed data, and differentiate the classes of faults, respectively. We evaluated the devised method using the NS2 simulator in various situations. One of the outcomes of the method is that the mobility of the nodes does not reduce the accuracy, in contrast to most of the traditional methods. Moreover, the evaluation demonstrates promising results for the networks with more than 50% faulty nodes. The results also show perfect performance in detecting permanent and intermittent faults in the networks with various percentage of faulty nodes.

Research and application of biological potency soft sensor modeling method in the industrial fed-batch chlortetracycline fermentation process

The potency of fermentation broth is one of the key parameters reflect the yield and quality of fermentation products of chlortetracycline (CTC). But so far, there is no instrument available on-line detection of CTC potency. All the tests are done by manual off-line testing, it takes several hours to sample and analyze the results from a fermentation tank (the production process usually has dozens of small, large fermenters). The use of analytical results to control the amount of operation will lead to severe lag. This paper combines self-organizing feature map (SOM) neural network with accurate classification of data and least squares support vector machine (LSSVM) algorithm with strong described the nonlinear characteristics. The SOM–LSSVM global modeling method of forecasting CTC fermentation potency is establish in this paper. According to the characteristics of nonlinear CTC fermentation process, just-in-time learning-recursive least squares support vector regression (JITL–RLSSVR) is used to perform local real-time modeling and 10-folding cross validation, and a hybrid soft sensor modeling method (JITL–RLSSVR + SOM–LSSVM) for online prediction of CTC fermentation potency is proposed in this paper. Field experiments show that this method can obtain more accurate potency prediction value, and it can meet the requirements of the production process.

Moisture soft sensor for batch fluid bed dryers: A practical approach

Moisture content is a critical quality attribute in drying of pharmaceutical formulations. This paper proposes a hybrid soft sensor for online real-time estimation of the product moisture in batch fluid bed dryers. Major applications include end-point detection, feed-back control, and process optimization resulting from increased process understanding. The proposed soft sensor utilizes commonly available measurements in a hybrid first-principle/empirical mathematical framework with few parameters to calibrate. Each parameter has a physical meaning in the model, enabling quantitative comparison of the drying dynamics of different formulations, products, and equipment. The soft sensor model requires experimental data from few batches for calibration, and historical data from production batches can be used for this purpose when available. Three case studies, two in pilot plant using different formulations and one using historical data from manufacturing batches, are presented in this paper. The results support the proposed soft sensor model as a robust, practical and accurate method for online estimation of moisture in fluid bed dryers.

Predicting influent biochemical oxygen demand: Balancing energy demand and risk management

Ready access to comprehensive influent information can help water reclamation plant (WRP) operators implement better real-time process controls, provide operational reliability and reduce energy consumption. The five-day biochemical oxygen demand (BOD5), a critical parameter for WRP process control, is expensive and difficult to measure using hard-sensors. An alternative approach based on a soft-sensor methodology shows promise, but can be problematic when used to predict high BOD5 values. Underestimating high BOD5 concentrations for process control could result in an insufficient amount of aeration, increasing the risk of an effluent violation. To address this issue, we tested a hierarchical hybrid soft-sensor approach involving multiple linear regression, artificial neural networks (ANN), and compromise programming. While this hybrid approach results in a slight decrease in overall prediction accuracy relative to the approach based on ANN only, the underestimation percentage is substantially lower (37% vs. 61%) for predictions of carbonaceous BOD5 (CBOD5) concentrations higher than the long-term average value. The hybrid approach is also flexible and can be adjusted depending on the relative importance between energy savings and managing the risk of an effluent violation.

Comprehensive approach to modeling and simulation of dynamic soft-sensing design for real-time building energy consumption

This research develops a multi-layer hybrid soft-sensor model to improve the accuracy of building thermal load prediction using integrated data. The multi-layer hybrid model (autoregressive and par...

A hybrid soft sensor for measuring hot-rolled strip temperature in the laminar cooling process

To overcome the difficulties associated with the frequently varying operating conditions of the laminar cooling process and measuring the strip temperature in the cooling process online, a soft sensor model of the hot-rolled strip is proposed which combines mathematical and hybrid intelligent methods. The proposed approach is based on computational intelligence techniques, where RBF neural networks, CBR and fuzzy logic reasoning are employed to estimate process parameters for predicting the coiling temperature of the strips. A number of simulation tests using industrial data are conducted where the desired numerical results are obtained. It has been shown that the proposed soft sensor has a high potential for being used to effectively measure the strip temperature in the laminar cooling process.