Soft sensor design using transductive moving window learner

Soft Sensor Modeling for 3D Transient Temperature Field of Large-Scale Aluminum Alloy Workpieces Based on Multi-Loss Consistency Optimization PINN

Uniform temperature distribution during quenching thermal treatment is crucial for achieving exceptional mechanical and physical properties of alloy materials. Accurate and rapid prediction of the 3D transient temperature field model of large-scale aluminum alloy workpieces is key to realizing effective thermal treatment. This paper establishes a 3D transient temperature field model of large aluminum alloy workpieces and proposes a multi-loss consistency optimization-based physics-informed neural network (MCO-PINN) to realize soft sensing of the 3D temperature field model. The method is based on a MLP structure and adopts Gaussian activation functions. A surrogate model of the partial differential equation (PDE) is first constructed, and the residuals of the PDE, initial and boundary conditions, and observed data are encoded into the loss functions of the network. By establishing a Gaussian probability distribution model of each loss function and combining it with maximum likelihood estimation, the weight consistency optimization method of each loss function is then proposed to further improve the approximation ability of the model. To optimize the training speed of the network, an adaptive initial-value-eigenvector coding clustering (AIV-ECC) algorithm is finally proposed, which quickly determines the parameters of the Gaussian activation function, reduces the dependence on the initial value and improves the generalization performance of the network. Simulation and industrial experiments demonstrate that the proposed MCO-PINN can solve the 3D transient temperature field model with high precision and high time efficiency based on sparse measurements.

Global interpretation and generalizability of boosted regression models for the prediction of methylene blue adsorption by different clay minerals and alkali activated materials

In this study, Gradient Boosted Regression Trees is applied, for the first time, to predict governing factors for methylene blue (MB) adsorption on a variety of adsorbents involving clay minerals, such as kaolinite and sepiolite together with industrial wastes red mud and fly ash, and alkali activated materials synthesized from aforementioned raw materials. Dataset was constructed using electronic databases, such as ScienceDirect, Scopus, Elsevier, and Google, experimental studies published between 2005 and 2022 were covered. The final dataset included experimental conditions, such as adsorbent type, adsorbent properties (surface characteristics, density, and chemical modifications), pH of the medium, adsorbent dosage, and temperature; and it involved 914 datapoints, which were extracted out of 75 papers (out of ∼1360 initially screened). Among distinct parameters, initial adsorbate concentration was found to be the most dominant factor affecting the MB uptake. Concordantly, pH of the solution medium, raw material selection, and modification types were also found to be significant in MB adsorption. Results showed that in terms of raw material and modification types, sepiolite and chemical (acid and/or alkaline modification) and thermal treatments, respectively, come forward as the most powerful candidates for enhanced MB adsorption performance. Modifications applied on adsorbents should be evaluated separately, as there is no general rule applicable for all experimental conditions, and the strength of the contribution of modification type also depends on initial adsorbate concentration. Implementation of various imputation methods showed the importance of reporting experimental factors, such as surface area, in the literature. Range of applicability of the suggested modeling procedure was assessed to help experimenters in testing MB uptake under novel experimental conditions.

Design of Soft-Sensing Model for Alumina Concentration Based on Improved Deep Belief Network

Alumina concentration is an important parameter in the production process of aluminum electrolysis. Due to the complex production environment in the industrial field and the complex physical and chemical reactions in the aluminum reduction cell, nowadays it is still unable to carry out online measurement and real-time monitoring. For solving this problem, a soft-sensing model of alumina concentration based on a deep belief network (DBN) is proposed. However, the soft-sensing model may have some limitations for different cells and different periodic working conditions such as local anode effect, pole changing, and bus lifting in the same cell. The empirical mode decomposition (EMD) and particle swarm optimization (PSO) with the DBN are combined, and an EMD–PSO–DBN method that can denoize and optimize the model structure is proposed. The simulation results show that the improved soft-sensing model improves the accuracy and universality of prediction.