A novel data-driven sampling strategy for optimizing industrial grinding operation under uncertainty using chance constrained programming

Sharma, Surbhi; Pantula, Priyanka Devi; Miriyala, Srinivas Soumitri; Mitra, Kishalay

doi:10.1016/j.powtec.2020.09.024

Cited by 37 publications

(11 citation statements)

References 40 publications

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“…The robustness strategy's primary goal is to determine the optimal amount of design factor setting for achieving a desired system’s response that is insensitive to uncertainty as a source of variability 13 , 35 . Some recent development in the field of robust optimization can be found in 36 – 39 . Besides, considerable effort has also been put into using stochastic programming and robust optimization approaches to address robust versions of wireless network design challenges, as seen in 40 – 43 .…”

Section: Background Of Study and Motivationsmentioning

confidence: 99%

A data driven approach in less expensive robust transmitting coverage and power optimization

Parnianifard

Mumtaz

Chaudhary

et al. 2022

Sci Rep

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This paper aims the development of a new reduced-cost algorithm for a multi-objective robust transmitter placement under uncertainty. Toward this end, we propose a new hybrid Kriging/Grey Wolf Optimizer (GWO) approach combined with robust design optimization to estimate the set of Pareto frontier by searching robustness as well as accuracy (lower objective function) in a design space. We consider minimization of the energy power consumption for transmitting as well as maximization of signal coverage in a multi-objective robust optimization model. The reliability of the model to control signal overlap for multiple transmitting antennas is also provided. To smooth computational cost, the proposed method instead of evaluating all receiver test points in each optimization iteration approximates signal coverages using Kriging interpolation to obtain optimal transmitter positions. The results demonstrate the utility and the efficiency of the proposed method in rendering the robust optimal design and analyzing the sensitivity of the transmitter placement problem under practically less-expensive computational efforts (350% and 320% less than computational time elapsed using standalone GWO and NSGAII respectively).

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Section: Background Of Study and Motivationsmentioning

confidence: 99%

A data driven approach in less expensive robust transmitting coverage and power optimization

Parnianifard

Mumtaz

Chaudhary

et al. 2022

Sci Rep

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“…In the case of grinding, investigations have already been developed where it was simulated and optimised under the influence of uncertainty [12,17]. In general, it is worth asking whether, in the uncertainty data of the PSD, PSDM, or PSCP, the first or second deviation of the uncertainty is considered (as appropriate), or whether the uncertainties defined are adequate for the WPES (as it can generate the largest/smallest deviations between what is simulated/optimised and reality).…”

Section: Methodsmentioning

confidence: 99%

“…The PMB could be defined as: "population balance or mass size balance, that describes a family of modelling techniques that includes tracking and manipulating partially or complete particle size distribution as they proceed through the comminution process" [6]; with this, it is possible to subdivide into two groups, the first group being related to the characterisation of the comminution process, which has been widely studied and constantly updated, mainly in terms of its mathematical expressions [13][14][15][16]. Meanwhile, the second group uses it as an integrated system, including control systems, hybrid optimisation, and optimisation with uncertainty [8,9,17]. All of these investigations use input data, which can be quantified in different ways while trying to be as precise as possible.…”

Section: Introductionmentioning

confidence: 99%

Experimental Uncertainty Analysis for the Particle Size Distribution for Better Understanding of Batch Grinding Process

2021

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Uncertainty in industrial processes is very common, but it is particularly high in the grinding process (GP), due to the set of interacting operating/design parameters. This uncertainty can be evaluated in different ways, but, without a doubt, one of the most important parameters that characterise all GPs is the particle size distribution (PSD). However, is the PSD a good way to quantify the uncertainty in the milling process? This is the question we attempt to answer in this paper. To do so, we use 10 experimental grinding repetitions, 3 grinding times, and 14 Tyler meshes (more than 400 experimental results). The most relevant results were compared for the weight percentage for each size (WPES), cumulative weight undersize (CWU), or the use of particle size distribution models (PSDM), in terms of continuous changes in statistical parameters in WPES for different grinding times. The probability distribution was found to be changeable when reporting the results of WPES/CWU/PSDM, we detected the over-/under-estimation of uncertainty when using WPES/CWU, and variations in the relationships between sizes were observed when using WPES/CWU. Finally, our conclusion was that the way in which the data are analysed is not trivial, due to the possible deviations that may occur in the uncertainty process.

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“…This shows that intelligent algorithms achieve excellent results in network hyperparameter optimization. − Uncertainty analysis is important compared to fixed value analysis because of uncertainties in data measurement, model fitting, and operating conditions. By considering the influence of multiple influencing factors to optimize the uncertain parameters, the validity of the uncertainty analysis is proven. − …”

Section: Introductionmentioning

confidence: 99%

Dense Residual LSTM-Attention Network for Boiler Steam Temperature Prediction with Uncertainty Analysis

et al. 2022

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Flexible operation of large-scale boilers for electricity generation is essential in modern power systems. An accurate prediction of boiler steam temperature is of great importance to the operational efficiency of boiler units to prevent the occurrence of overtemperature. In this study, a dense, residual long short-term memory network (LSTM)-attention model is proposed for steam temperature prediction. In particular, the residual elements in the proposed model have a great advantage in improving the accuracy by adding short skip connections between layers. To provide overall information for the steam temperature prediction, uncertainty analysis based on the proposed model is performed to quantify the uncertainties in steam temperature variations. Our results demonstrate that the proposed method exhibits great performance in steam temperature prediction with a mean absolute error (MAE) of less than 0.6 °C. Compared to algorithms such as support-vector regression (SVR), ridge regression (RIDGE), the recurrent neural network (RNN), the gated recurrent unit (GRU), and LSTM, the prediction accuracy of the proposed model outperforms by 32, 16, 12, 10, and 11% in terms of MAE, respectively. According to our analysis, the dense residual LSTM-attention model is shown to provide an accurate early warning of overtemperature, enabling the development of real-time steam temperature control.

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A novel data-driven sampling strategy for optimizing industrial grinding operation under uncertainty using chance constrained programming

Cited by 37 publications

References 40 publications

A data driven approach in less expensive robust transmitting coverage and power optimization

A data driven approach in less expensive robust transmitting coverage and power optimization

Experimental Uncertainty Analysis for the Particle Size Distribution for Better Understanding of Batch Grinding Process

Dense Residual LSTM-Attention Network for Boiler Steam Temperature Prediction with Uncertainty Analysis

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