Amination of biochar surface from watermelon peel for toxic chromium removal enhancement

Watermelon peel residues were used to produce a new biochar by dehydration method.The new biochar has undergone two methods of chemical modification and the effect of this chemical modification on its ability to adsorb Cr(VI)ions from aqueous solution has been investigated.Three biochars,Melon-B,Melon-BO-NH_(2) and Melon-BO-TETA,were made from watermelon peel via dehydration with 50%sulfuric acid to give Melon-B followed by oxidation with ozone and amination using ammonium hydroxide to give Melon-BO-NH_(2) or Triethylenetetramine(TETA)to give Melon-BO-TETA.The prepared biochars were characterized by BET,BJH,SEM,FT-IR,TGA,DSC and EDAX analyses.The highest removal percentage of Cr(VI)ions was 69%for Melon-B,98%for Melon-BO-NH_(2) and 99%for Melon-BO-TETA biochars of 100 mg·L^(−1) Cr(VI)ions initial concentration and 1.0 g·L^(−1) adsorbents dose.The unmodified biochar(Melon-B)and modified biochars(Melon-BO-NH_(2) and Melon-BO-TETA)had maximum adsorption capacities(Q_(m))of 72.46,123.46,and 333.33 mg·g^(−1),respectively.The amination of biochar reduced the pore size of modified biochar,whereas the surface area was enhanced.The obtained data of isotherm models were tested using different error function equations.The Freundlich,Tempkin and Langmuir isotherm models were best fitted to the experimental data of Melon-B,Melon-BO-NH_(2) and Melon-BO-TETA,respectively.The adsorption rate was primarily controlled by pseudo-second–order rate model.Conclusively,the functional groups interactions are important for adsorption mechanisms and expected to control the adsorption process.The adsorption for the Melon-B,Melon-BO-NH_(2) and Melon-BO-TETA could be explained for acid–base interaction and hydrogen bonding interaction.

Water quality soft-sensor prediction in anaerobic process using deep neural network optimized by Tree-structured Parzen Estimator

Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants(WWTPs).However,some water quality metrics are not measurable in real time,thus influencing the judgment of the operators and may increase energy consumption and carbon emission.One of the solutions is using a soft-sensor prediction technique.This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit(BiGRU)combined with Gaussian Progress Regression(GPR)optimized by Tree-structured Parzen Estimator(TPE).TPE automatically optimizes the hyperparameters of BiGRU,and BiGRU is trained to obtain the point prediction with GPR for the interval prediction.Then,a case study applying this prediction method for an actual anaerobic process(2500 m^(3)/d)is carried out.Results show that TPE effectively optimizes the hyperparameters of BiGRU.For point prediction of CODeff and biogas yield,R^(2)values of BiGRU,which are 0.973 and 0.939,respectively,are increased by 1.03%–7.61%and 1.28%–10.33%,compared with those of other models,and the valid prediction interval can be obtained.Besides,the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation.It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption.