On-line estimation of key process variables based on kernel partial least squares in an industrial cokes wastewater treatment plant

Woo, Seung Han; Jeon, Che Ok; Yun, Yeoung‐Sang; Choi, Hyeoksun; Lee, Chang‐Soo; Lee, Dong Hoon

doi:10.1016/j.jhazmat.2008.04.004

Cited by 38 publications

(18 citation statements)

References 28 publications

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“…For that purpose, part of the training dataset is kept out of the model, predicted and finally compared with the actual values (using CV). A more detailed explanation of the PLS algorithm could be found elsewhere [28][29][30][31]. However, it was found that the number of LVs depicted by this methodology did not allow an acceptable training model.…”

Section: Multivariate Statistical Analysismentioning

confidence: 99%

Polyhydroxyalkanoate granules quantification in mixed microbial cultures using image analysis: Sudan Black B versus Nile Blue A staining

Mesquita

Amaral

Leal

et al. 2015

Analytica Chimica Acta

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Polyhydroxyalkanoates (PHA) can be produced and intracellularly accumulated as inclusions by mixed microbial cultures (MMC) for bioplastic production and in enhanced biological phosphorus removal (EBPR) systems. Classical methods for PHA quantification use a digestion step prior to chromatography analysis, rendering them labor intensive and time-consuming. The present work investigates the use of two quantitative image analysis (QIA) procedures specifically developed for PHA inclusions identification and quantification. MMC obtained from an EBPR system were visualized by bright-field and fluorescence microscopy for PHA inclusions detection, upon Sudan Black B (SBB) and Nile Blue A (NBA) staining, respectively. The captured color images were processed by QIA techniques and the image analysis data were further treated using multivariate statistical analysis. Partial least squares (PLS) regression coefficients of 0.90 and 0.86 were obtained between QIA parameters and PHA concentrations using SBB and NBA, respectively. It was found that both staining procedures might be seen as alternative methodologies to classical PHA determination.

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Section: Multivariate Statistical Analysismentioning

confidence: 99%

Polyhydroxyalkanoate granules quantification in mixed microbial cultures using image analysis: Sudan Black B versus Nile Blue A staining

Mesquita

Amaral

Leal

et al. 2015

Analytica Chimica Acta

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“…Some others soft sensors applications for primary variables have been referred to using neural networks (Hamed et al 2004) multivariate statistical processes (Woo et al 2009) or both (Lee et al 2008) all of them based on expensive on-line information comprising, for instance, influent measurements of COD, TN and TSS.…”

Section: Introductionmentioning

confidence: 99%

Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network

Cañete

Saz-Orozco

Baratti

et al. 2016

Expert Systems with Applications

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“…Among them, coke wastewater is considered to be the most toxic one; therefore, it must be thoroughly treated, usually by a combination of physicochemical and biological treatments (Marañόn et al 2008), before being discharged into the environment (Zhang et al 1998;Kumar et al 2003;Kim et al 2007;Park et al 2007;Woo et al 2009). Coke wastewater, largely generated in the cooling step after coking coals at high temperature (900-1100°C) and in the liquid-stripping step of coke oven gas (Kim et al 2008), is generated in coal coking, coal gas purification, and by-product recovery processes of cokemaking factories in the iron-steel-producing industries (Zhang et al 1998).…”

Section: Introductionmentioning

confidence: 99%

Life cycle inventory processes of the integrated steel plant (ISP) in Krakow, Poland—coke production, a case study

Bieda

Grzesik

Sala

et al. 2015

Int J Life Cycle Assess

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Purpose The goal of this paper is to describe the life cycle inventory (LCI) approach to the cokemaking process in the Integrated Steel Plant's Coke Oven Battery (ISPCOB) in Kraków, Poland. The system boundaries were labeled as gate-to-gate, covering the full chain process of coke production. The background input and output data from the coke production process (CPP) has been inventoried. Methods The functional unit (FU) was produced of 1 Mg of the coke in ISPCOB. The following inputs and outputs have been taken into account in the LCI study: product (coke), all intermediate products, by-products, and emissions. Raw fuel mining and means of external transportation of natural gas, manufacture of downstream products, their use, end of life, and infrastructure were not included. Time coverage is 2004, because since 2005, robust inventory data coverage of full ISP coke processes are lacking. Data used for inventory calculation are affected by uncertainty.Results and discussion With regard to 1 Mg of coke produced in ISPCOB in 2004, the consumption of coke coal and coke oven gas amounted to 1.35 Mg and 195 m 3 , respectively. The main materials, fuels, and energy used for the coke production were electric energy, steam, air, heat, and industrial water. Emissions of SO 2 , NO 2 , CO, CO 2 , NH 3 , aliphatic hydrocarbons, HCN, H 2 S,

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On-line estimation of key process variables based on kernel partial least squares in an industrial cokes wastewater treatment plant

Cited by 38 publications

References 28 publications

Polyhydroxyalkanoate granules quantification in mixed microbial cultures using image analysis: Sudan Black B versus Nile Blue A staining

Polyhydroxyalkanoate granules quantification in mixed microbial cultures using image analysis: Sudan Black B versus Nile Blue A staining

Soft-sensing estimation of plant effluent concentrations in a biological wastewater treatment plant using an optimal neural network

Life cycle inventory processes of the integrated steel plant (ISP) in Krakow, Poland—coke production, a case study

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