Combined Size and Shape Optimization of Structures with DOE,RSM and GA

In this paper,size and shape optimization problem of a machine gun system is addressed with an efficient hybrid method,in which a novel and flexible mesh morphing technique is employed to achieve fast parameterization and modification of complexity structure without going back to CAD for reconstruction of geometric models or to finite element analysis（ FEA） for remodeling. Design of experiments（ DOE） and response surface method（ RSM） are applied to approximate the constitutive parameters of a machine gun system based on experimental tests. Further FEA,secondary development technique and genetic algorithm（ GA） are introduced to find all the optimal solutions in one go and the optimal design of the demonstrated machine gun system is obtained. Results of the rigid-flexible coupling dynamic analysis and exterior ballistics calculation validate the proposed methodology,which is relatively time-saving,reliable and has the potential to solve similar problems.

Ethanol mediated As(Ⅲ) adsorption onto Zn-loaded pinecone biochar：Experimental investigation,modeling,and optimization using hybrid artificial neural network-genetic algorithm approach

Organic matters（OMs） and their oxidization products often influence the fate and transport of heavy metals in the subsurface aqueous systems through interaction with the mineral surfaces. This study investigates the ethanol（EtO H）-mediated As（Ⅲ） adsorption onto Zn-loaded pinecone（PC） biochar through batch experiments conducted under Box–Behnken design. The effect of EtO H on As（Ⅲ） adsorption mechanism was quantitatively elucidated by fitting the experimental data using artificial neural network and quadratic modeling approaches. The quadratic model could describe the limiting nature of EtO H and pH on As（Ⅲ） adsorption,whereas neural network revealed the stronger influence of Et OH（64.5%） followed by pH（20.75%）and As（Ⅲ） concentration（14.75%） on the adsorption phenomena. Besides, the interaction among process variables indicated that Et OH enhances As（Ⅲ） adsorption over a pH range of2 to 7, possibly due to facilitation of ligand–metal（Zn） binding complexation mechanism.Eventually, hybrid response surface model–genetic algorithm（RSM–GA） approach predicted a better optimal solution than RSM, i.e., the adsorptive removal of As（Ⅲ）（10.47 μg/g） is facilitated at 30.22 mg C/L of Et OH with initial As（Ⅲ） concentration of 196.77 μg/L at pH 5.8. The implication of this investigation might help in understanding the application of biochar for removal of various As（Ⅲ） species in the presence of OM.

Investigation of colloidal biogenic sulfur flocculation:Optimization using response surface analysis

The colloidal properties of biogenic elemental sulfur（S^0）cause solid–liquid separation problems,such as poor settling and membrane fouling.In this study,the separation of S^0 from bulk liquids was performed using flocculation.Polyaluminum chloride（PAC）,polyacrylamide（PAM）and microbial flocculant（MBF）were compared to investigate their abilities to flocculate S^0 produced during the treatment of sulfate-containing wastewater.A novel approach with response surface methodology（RSM）was employed to evaluate the effects and interactions of flocculant dose,pH and stirring intensity,on the treatment efficiency in terms of the S^0 flocculation and the supernatant turbidity removal.The dose optimization results indicated that the S^0 flocculation efficiency decreased in the following order PAC〉MBF〉PAM.Optimum S^0 flocculation conditions were observed at pH 4.73,a stirring speed of 129 r/min and a flocculant dose of 2.42 mg PAC/mg S.During optimum flocculation conditions,the S^0f locculation rate reached 97.53%.Confirmation experiments demonstrated that employing PAC for S^0 flocculation is feasible and RSM is an efficient approach for optimizing the process of S^0 flocculation.The results provide basic parameters and conditions for recovering sulfur during the treatment of sulfate-laden wastewaters.

Modeling and multi-response optimization of machining performance while turning hardened steel with self-propelled rotary tool

There are many advanced tooling approaches in metal cutting to enhance the cutting tool performance for machining hard-to-cut materials. The self propelled rotary tool （SPRT） is one of the novel approaches to improve the cutting tool performance by providing cutting edge in the form of a disk, which rotates about its principal axis and provides a rest period for the cutting edge to cool and allow engaging a fresh cutting edge with the work piece. This paper aimed to present the cutting performance of SPRT while turning hardened EN24 steel and optimize the machining conditions. Surface roughness （Ra） and metal removal rate （rMMR） are considered as machining perfor- mance parameters to evaluate, while the horizontal incli- nation angle of the SPRT, depth of cut, feed rate and spindle speed are considered as process variables. Initially, design of experiments （DOEs） is employed to minimize the number of experiments. For each set of chosen process variables, the machining experiments are conducted on computer numerical control （CNC） lathe to measure the machining responses. Then, the response surface method- ology （RSM） is used to establish quantitative relationships for the output responses in terms of the input variables. Analysis of variance （ANOVA） is used to check the adequacy of the model. The influence of input variables on the output responses is also determined. Consequently, these models are formulated as a multi-response optimi- zation problem to minimize the Ra and maximize the rMMR simultaneously. Non-dominated sorting genetic algorithm-II （NSGA-II） is used to derive the set of Pareto-optimal solutions. The optimal results obtained through the pro- posed methodology are also compared with the results of validation experimental runs and good correlation is found between them.