Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Journal of Material Cycles and Waste Management
Published in: Journal of Material Cycles and Waste Management 1/2020

26-10-2019 | ORIGINAL ARTICLE

PSO–ANN-based prediction of cobalt leaching rate from waste lithium-ion batteries

Authors: Hossein Ebrahimzade, Gholam Reza Khayati, Mahin Schaffie

Published in: Journal of Material Cycles and Waste Management | Issue 1/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Leaching is a complex solid–liquid reaction which has an important influence on the recovery efficiency of the spent lithium-ion batteries (LIBs). Therefore, it is of significant importance to utilize an appropriate technique to predict the effect of operating parameters on the optimized recovery rate. In the present study, a combined method of the artificial neural network (ANN) and particle swarm optimization algorithm (PSO) was used as a model to predict the leaching efficiency of cobalt from spent LIBs. To find the dependency of the leached percentage of cobalt on the operational parameters as model inputs, 42 repeatable numerous experiments are performed using H2SO4 in the presence of H2O2. It was found that the proposed model can be a useful technique in the demonstration of the nonlinear relationship between the leaching efficiency and the process parameters. The performance of PSO–ANN models was validated by statistical thresholds and compared with those of common ANN technique. Moreover, it was found that the pulp density of the leaching solution and the concentration of sulfuric acid were the most important reaction parameters of the spent LIBs recovery, respectively.
previous article Transformation of di-n-butyl phthalate in an anaerobic/anoxic/oxic leachate treatment process
next article Integrated solid-waste management for Kabul city, Afghanistan

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Literature
1.
Zhang X, Xie Y, Lin X et al (2013) An overview on the processes and technologies for recycling cathodic active materials from spent lithium-ion batteries. J Mater Cycles Waste Manag 15:420–430CrossRef
2.
Zhang P, Yokoyama T, Itabashi O et al (1998) Hydrometallurgical process for recovery of metal values from spent lithium-ion secondary batteries. Hydrometallurgy 47:259–271CrossRef
3.
Dorella G, Mansur MB (2007) A study of the separation of cobalt from spent Li-ion battery residues. J Power Sources 170:210–215CrossRef
4.
Pant D, Dolker T (2017) Green and facile method for the recovery of spent lithium nickel manganese cobalt oxide (NMC) based lithium ion batteries. Waste Manag 60:689–695CrossRef
5.
Li L, Zhai L, Zhang X et al (2014) Recovery of valuable metals from spent lithium-ion batteries by ultrasonic-assisted leaching process. J Power Sources 262:380–385CrossRef
6.
Badawy SM, Nayl AA, El Khashab RA, El-Khateeb MA (2014) Cobalt separation from waste mobile phone batteries using selective precipitation and chelating resin. J Mater Cycles Waste Manag 16:739–746CrossRef
7.
Libraries T (2017) Sustainable recovery of cathode materials from spent lithium-ion batteries using lactic acid leaching system. ACS Sustain Chem Eng 5:5224–5233CrossRef
8.
Honório KM, De Lima EF, Quiles MG et al (2010) Artificial neural networks and the study of the psychoactivity of cannabinoid compounds. Chem Biol Drug Design 75:632–640CrossRef
9.
Marini F, Bucci R, Magrì AL, Magrì AD (2008) Artificial neural networks in chemometrics: History, examples and perspectives. Microchem J 88:178–185CrossRef
10.
Taylor P, Kundu P, Debsarkar A et al (2014) Artificial neural network modelling in biological removal of organic carbon and nitrogen for the treatment of slaughterhouse wastewater in a batch reactor. Environ Technol 35:1296–1306CrossRef
11.
Khataee A, Fathinia M, Rad TS (2016) Kinetic modeling of nalidixic acid degradation by clinoptilolite nanorod-catalyzed ozonation process. RSC Adv 6:44371–44382CrossRef
12.
Thakur V, Ramesh A (2018) Analyzing composition and generation rates of biomedical waste in selected hospitals of Uttarakhand, India. J Mater Cycles Waste Manag 20:877–890CrossRef
13.
Galván IM, Zaldívar JM, Hernández H, Molga E (1996) The use of neural networks for fitting complex kinetic data. Comput Chem Eng 20:1451–1465CrossRef
14.
Normandin A, Grandjean BPA, Thibault J (1993) PVT data analysis using neural network models. Ind Eng Chem Res 32:970–975CrossRef
15.
Aldrich C, Deventer J, Reuteri MA (1994) The application of neural nets in the metallurgical industry. Miner Eng 7:793–809CrossRef
16.
Ijadpanah-Saravi H, Safari M, Noruzi-Masir B et al (2017) Intelligent tools to model photocatalytic degradation of beta-naphtol by titanium dioxide nanoparticles. J Chemom 31:e2907CrossRef
17.
Lazzús JA (2010) Prediction of flash point temperature of organic compounds using a hybrid method of group contribution + neural network + particle swarm optimization. Chin J Chem Eng 18:817–823CrossRef
18.
Momeni E, Armaghani DJ, Hajihassani M (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63CrossRef
19.
Roh S-B, Oh S-K, Park E-K, Choi WZ (2017) Identification of black plastics realized with the aid of Raman spectroscopy and fuzzy radial basis function neural networks classifier. J Mater Cycles Waste Manag 19:1093–1105CrossRef
20.
Rao R, Sahu JN (2018) Modeling and optimization by particle swarm embedded neural network for adsorption of zinc (II) by palm kernel shell based activated carbon from aqueous environment. J Environ Manag 206:178–191CrossRef
21.
Xia B, Cui D, Sun Z et al (2018) State of charge estimation of lithium-ion batteries using optimized Levenberg–Marquardt wavelet neural network. Energy 153:694–705CrossRef
22.
Khajeh M, Kaykhaii M, Hossein S, Shakeri M (2014) Particle swarm optimization—artificial neural network modeling and optimization of leachable zinc from flour samples by miniaturized homogenous liquid–liquid microextraction. J Food Compos Anal 33:32–38CrossRef
23.
Ahmadi M-A, Ahmad Z, Phung LTK et al (2016) Estimation of water content of natural gases using particle swarm optimization method. Pet Sci Technol 34:595–600CrossRef
24.
Khajeh M, Dastafkan K (2014) Removal of molybdenum using silver nanoparticles from water samples: particle swarm optimization–artificial neural network. J Ind Eng Chem 20:3014–3018CrossRef
25.
Ghaedi M, Ghaedi AM, Ansari A et al (2014) Artificial neural network and particle swarm optimization for removal of methyl orange by Gold nanoparticles loaded on activated carbon and Tamarisk. Spectrochim Acta Part A Mol Biomol Spectrosc 132:639–654CrossRef
26.
Sheikhan M, Pardis R, Gharavian D (2013) State of charge neural computational models for high energy density batteries in electric vehicles. Neural Comput Appl 22:1171–1180CrossRef
27.
Rahman A, Anwar S, Izadian A (2016) Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method. J Power Sources 307:86–97CrossRef
28.
Agarwal S, Tyagi I, Kumar V et al (2016) Kinetics and thermodynamics of methyl orange adsorption from aqueous solutions—artificial neural network-particle swarm optimization modeling. J Mol Liquid 218:354–362CrossRef
29.
Mansouri I, Shahri A, Zahedifar H (2016) A new algorithm in nonlinear analysis of structures using particle swarm optimization. IIUM Eng J 17:157–168CrossRef
30.
Wang W-Y, Yen CH, Lin J-L, Xu R-B (2019) Recovery of high-purity metallic cobalt from lithium nickel manganese cobalt oxide (NMC)-type Li-ion battery. J Mater Cycles Waste Manag 21(2):300–307CrossRef
31.
Jha MK, Kumari A, Jha AK et al (2013) Recovery of lithium and cobalt from waste lithium ion batteries of mobile phone. Waste Manag 33:1890–1897CrossRef
32.
Joo S, Shin D, Oh C et al (2016) Selective extraction of nickel from cobalt, manganese and lithium in pretreated leach liquors of ternary cathode material of spent lithium-ion batteries using synergism caused by Versatic 10 acid and LIX 84-I. Hydrometallurgy 159:65–74CrossRef
33.
Gao W, Liu C, Cao H et al (2018) Comprehensive evaluation on effective leaching of critical metals from spent lithium-ion batteries. Waste Manag 75:477–485CrossRef
34.
Ebrahimzade H, Khayati GR, Schaffie M (2018) Leaching kinetics of valuable metals from waste Li-ion batteries using neural network approach. J Mater Cycles Waste Manag 20:2117–2129CrossRef
35.
Grenman H, Salmi T, Murzin DY (2011) Solid–liquid reaction kinetics—experimental aspects and model development. Rev Chem Eng 27:53–77CrossRef
36.
Meshram P, Abhilash A, Pandey BD et al (2019) Extraction of metals from spent lithium ion batteries—role of acid, reductant and process intensification in recycling. Indian J Chem Technol 25:368–375
37.
Touboul M, Le Samedi E, Sephar N et al (1993) Binary systems with Li2SO4 as one of the components. J Therm Anal Calorim 40:1151–1156CrossRef
38.
Meshram P, Pandey BD, Mankhand TR, Deveci H (2016) Acid baking of spent lithium ion batteries for selective recovery of major metals: a two-step process. J Ind Eng Chem 43:117–126CrossRef
39.
Takacova Z, Havlik T, Kukurugya F, Orac D (2016) Cobalt and lithium recovery from active mass of spent Li-ion batteries : theoretical and experimental approach. Hydrometallurgy 163:9–17CrossRef
40.
He LP, Sun SY, Song XF, Yu JG (2017) Leaching process for recovering valuable metals from the LiNi1/3Co1/3Mn1/3O2 cathode of lithium-ion batteries. Waste Manag 64:171–181CrossRef
Metadata
Title
PSO–ANN-based prediction of cobalt leaching rate from waste lithium-ion batteries
Authors
Hossein Ebrahimzade
Gholam Reza Khayati
Mahin Schaffie
Publication date
26-10-2019
Publisher
Springer Japan
Published in
Journal of Material Cycles and Waste Management / Issue 1/2020
Print ISSN: 1438-4957
Electronic ISSN: 1611-8227
DOI
https://doi.org/10.1007/s10163-019-00933-2

Other articles of this Issue 1/2020

Journal of Material Cycles and Waste Management 1/2020 Go to the issue

ORIGINAL ARTICLE

NOx scrubbing with H2O2/HNO3 solutions achieved with a laboratory bubble contactor

ORIGINAL ARTICLE

On the use of grains recovered from spent vitrified wheels in resinoid applications

ORIGINAL ARTICLE

The usage of novel acrylic-modified water-reducible alkyd resin obtained from post-consumer PET bottles in water-based paint formulation

ORIGINAL ARTICLE

Simultaneous synergistic effects of addition of agro-based adsorbent on anaerobic co-digestion of food waste and sewage sludge

ORIGINAL ARTICLE

Analysis of the comprehensive management of sewage sludge in Poland

ORIGINAL ARTICLE

Transformation of di-n-butyl phthalate in an anaerobic/anoxic/oxic leachate treatment process