Table 9 Values of error functions of adsorption isotherm models of 3-Nph on CM-HC at different temperatures.

From: Deep learning artificial neural network framework to optimize the adsorption capacity of 3-nitrophenol using carbonaceous material obtained from biomass waste

T (K)

Model

Error functions

APE

SSE

∆q (%)

χ2

EABS

RMSE

300.15

Langmuir

8.279

33.754

0.118

1.274

12.653

2.598

Freundlich

57.478

469.224

1.052

16.197

54.511

9.687

Temkin

10.216

99.680

0.151

2.364

21.640

4.465

Redlich–Peterson

11.258

19.641

0.187

1.190

11.190

1.982

313.15

Langmuir

18.906

144.054

0.299

11.766

24.773

5.368

Freundlich

37.341

235.752

0.666

9.589

37.525

6.867

Temkin

15.791

142.297

0.278

4.066

27.083

5.335

Redlich–Peterson

9.271

26.502

0.160

1.585

11.177

2.302

330.15

Langmuir

14.555

182.855

0.182

4.808

30.997

6.047

Freundlich

51.710

672.224

0.910

17.772

62.448

11.595

Temkin

20.789

323.810

0.339

14.212

36.983

8.047

Redlich–Peterson

17.807

122.489

0.272

11.221

21.007

4.950

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