Factorial experimental design for recovering heavy metals from sludge with ion-exchange resin

Thirty-seven years of archives of environmental protection

“…Statistical design of experiments is a simple and systematic method to optimize a design for process performance, quality and cost of products. Statistics design of experiments can cover a larger area of experimental statistics and obtain unambiguous results at the minimum expense [25,26]. Factorial design is a standard technique and widely used for studying a random response to a set of k possible factors [25,26].…”

“…The ecology, biochemistry and the genome of A. hydrophila has been very well researched [7,24]. In their efforts to analyze the genetic diversity of A. hydrophila, most researchers have focused on taxonomic aspects of identifi cation [10], mutations in drug resistance genes [12], selected sequences responsible for virulence [5], transposable genetic elements [6] and repeated sequences [19,25,30]. There is a general scarcity of information about the diversity of the A. hydrophila genome, with the exception of selected, non-representative and well-researched regions [26].…”

Sorption properties of the iminodiacetate ion exchange resin, amberlite IRC‐718, toward divalent metal ions

“…7,28 The macroreticular structure of Amberlite IRC-718 provides a number of advantages over traditional gel resins; it is highly resistant to osmotic shock and has improved kinetics of ion exchange. 28 Amberlite IRC-718 was used for several purposes such as extraction of heavy metals (Cu 21 , Ni 21 , Fe 21 , and so forth) from solutions like wine, 29 liquor, 30 stored phenol, 31 sludge, 32 and for ion exchange chromatography. 33 In some studies, it was considered as a reference to study other resins.…”

Process optimization of nickel extraction from hazardous waste

“…Statistics design of experiments can cover a larger area of experimental statistics and obtain unambiguous results at the minimum expense [25,26]. Factorial design is a standard technique and widely used for studying a random response to a set of k possible factors [25,26]. With the factorial design methodology, main effects, interaction effects and low-order interactions may be estimated [27,28].…”