Recovery prediction of copper oxide ore column leaching by hybrid neural genetic algorithm

The artificial neural network (ANN) and hybrid of artificial neural network and genetic algorithm (GANN) were applied to predict the optimized conditions of column leaching of copper oxide ore with relations of input and output data. The leaching experiments were performed in three columns with the heights of 2, 4 and 6 m and in particle size of <25.4 and <50.8 mm. The effects of different operating parameters such as column height, particle size, acid flow rate and leaching time were studied to optimize the conditions to achieve the maximum recovery of copper using column leaching in pilot scale. It was found that the recovery increased with increasing the acid flow rate and leaching time and decreasing particle size and column height. The efficiency of GANN and ANN algorithms was compared with each other. The results showed that GANN is more efficient than ANN in predicting copper recovery. The proposed model can be used to predict the Cu recovery with a reasonable error.     

Sonochemical Synthesis and Characterization of the New Plate-Shaped Lead(II)–Iodo Coordination Polymer: A Precursor to Produce a Pure Phase Nano-Sized Lead(II) Oxide

Preparation of plate-shaped nanostructures of a new 1D polymeric lead(II) complex containing the Pb₂-(μ-I)₂ motif, [Pb(neo)I₂] _n ,(1) where neo is the abbreviation of neocuproine, using a sonochemical method is described. The new coordination polymer is characterized by scanning electron microscopy, X-ray diffraction (XRD), elemental analysis and infrared spectroscopy. The single crystalline material is obtained using a heat gradient applied to a solution of the reagents. Single-crystal XRD analysis indicates that a coordination number of six for Pb^II ions, (i.e., PbN₂I₄) with an asymmetrical coordination sphere and “stereo-chemically active” electron lone pairs. They also show that the chains interact with each other through π–π stacking interactions creating a 3D framework. PbO nanoparticles are obtained by thermolysis of 1 at 180?°C with oleic acid as a surfactant. Scanning electron microscopy confirms formation of PbO particles with a size of ~25?nm.     

Process optimisation and mechanical properties of friction stir lap welds of 7075-T6 stringers on 2024-T3 skin

This paper focuses on the results of process optimisation and mechanical tests that were used to ascertain the feasibility of using friction stir welding (FSW) to join stringers to skin. The effects of process parameters on weld quality of 1.5-mm 7075-T6 stringers lap-joined on 2.3-mm 2024-T3 skins were investigated. Advancing and retreating side locations on the joint configuration were alternated to determine optimal design arrangements. The effects of travel and rotation speeds on weld quality and defect generation were also investigated. Weld quality was assessed by optical microscopy and bending tests. It was found that: (i) the increase of the welding speed or the decrease of the rotational speed resulted in a reduction of the hooking size and top plate thinning but did not eliminated them, (ii) double pass welds by overlapping the advancing sides improved significantly the weld quality by overriding the hooking defect, and (iii) change of the rotational direction for a counter clockwise with a left-threaded probe eliminated the top sheet thinning defect. Subsequently, FSW lap joints were produced using optimum conditions and underwent extensive mechanical testing program. Several assembly configurations including discontinuous and continuous welds as well as single and double pass welds were produced. The results obtained for cyclic fatigue performance of FSW panels are compared with riveted lap joints of identical geometry. S–N curves, bending behaviour, failure locations and defect characterisation are also discussed. It was found that: (i) the tensile strength of FSW joints approached that of the base material but with a significant reduction in the fatigue life, (ii) the probe plunge and removal locations served as the key crack nucleation sites in specimens with discontinuous welds, and (iii) double pass welds with overlapping advancing sides showed outstanding fatigue life and very good tensile properties. The present work provided some valuable insight into both the fabrication and application of FSW on stringer/skin lap joints.     

Determination of selenium in natural waters by adsorptive differential pulse cathodic stripping voltammetry

In this work bovine albumin was used innovatively as a medium for adsorptive accumulation of Se–I₂ on thin mercury film electrode. Se–I₂ was formed by reaction between Se(IV) and iodide in HCl media. The adsorbed Se–I₂ was stripped in 0.05 M HCl by differential pulse cathodic potential scan. The proposed method was successfully applied to analysis of Se(IV) and Se(VI) in natural waters sampled from some lagoons south of Caspian Sea. The optimum reaction conditions and other analytical parameters and influence of cations and anions were studied. The detection limit was 0.37 ng mL⁻¹. The obtained results were compared with the results of DPCSV after electrochemical preconcentration, HG-AAS and ICP-AES.     

Monitoring and maintaining the water clarity of salinity gradient solar ponds

A common problem encountered in salinity-gradient solar ponds is the growth of various types of algae and bacterial populations, which affects the brine clarity and hence reduces thermal performance. Algae and bacterial populations are enhanced by the presence of organic nutrient such as nitrogen and phosphorus. A comprehensive study was undertaken on three salinity-gradient solar ponds in Australia: a 3000 m² sodium chloride solar pond at Pyramid Hill in Northern Victoria; a 50 m² sodium chloride; and 15 m² magnesium chloride solar pond at RMIT University in Bundoora, Victoria. The experimental study involved monitoring the clarity of these three ponds and testing chemical and biological treatment methods to see their effect on the brine transparency. The sources of turbidity and their impacts on clarity and efficiency of salinity-gradient solar ponds are presented in detail in this paper. The initial observation showed that the amount of sunlight is reduced due to the heavy algal growth creating instability in the solar pond as it absorbs light. Two treatment methods were applied to these solar ponds and experiments were conducted to study the turbidity reduction in the solar ponds. In the RMIT magnesium chloride solar pond, diluted hydrochloric acid was injected in the pond to reduce the pH and turbidity levels. Algal blooms were observed and found in the pond where the pH was between 5.5 and 8. It was observed from the experimental study that pH values should be kept below 4.5 to maintain low turbidity and prevent algae growth. The introduction of brine shrimps was also found to be very effective and economical to control algae, provided the oxygen has not depleted due to advanced heavy algal growth. The investigation concluded that hydrochloric acid could be used initially as a shock treatment to kill all the algae and then brine shrimps could be introduced to control the growth of algal and maintain transparency. This analysis showed that by using a combination of chemical and biological treatment methods, the pond clarity can be maintained and the thermal efficiency of the solar pond can be improved.     

<Emphasis Type="Italic">κ</Emphasis>-<Emphasis Type="Italic">PMP</Emphasis>: Enhancing Physics-based Motion Planners with Knowledge-Based Reasoning

Physics-based motion planning is a challenging task, since it requires the computation of the robot motions while allowing possible interactions with (some of) the obstacles in the environment. Kinodynamic motion planners equipped with a dynamic engine acting as state propagator are usually used for that purpose. The difficulties arise in the setting of the adequate forces for the interactions and because these interactions may change the pose of the manipulatable obstacles, thus either facilitating or preventing the finding of a solution path. The use of knowledge can alleviate the stated difficulties. This paper proposes the use of an enhanced state propagator composed of a dynamic engine and a low-level geometric reasoning process that is used to determine how to interact with the objects, i.e. from where and with which forces. The proposal, called κ-PMP can be used with any kinodynamic planner, thus giving rise to e.g. κ-RRT. The approach also includes a preprocessing step that infers from a semantic abstract knowledge described in terms of an ontology the manipulation knowledge required by the reasoning process. The proposed approach has been validated with several examples involving an holonomic mobile robot, a robot with differential constraints and a serial manipulator, and benchmarked using several state-of-the art kinodynamic planners. The results showed a significant difference in the power consumption with respect to simple physics-based planning, an improvement in the success rate and in the quality of the solution paths.     

The finite cell method for nearly incompressible finite strain plasticity problems with complex geometries

In this paper, the performance of the Finite Cell Method is studied for nearly incompressible finite strain plasticity problems. The Finite Cell Method is a combination of the fictitious domain approach with the high-order Finite Element Method. It provides easy mesh generation capabilities for highly complex geometries; moreover, this method offers high convergence rates, the possibility to overcome locking and robustness against high mesh distortions. The performance of this method is numerically investigated based on computations of benchmark and applied problems. The results are also verified with the $h$- and $p$-version Finite Element Method. It is demonstrated that the Finite Cell Method is an appropriate simulation tool for large plastic deformations of structures with complex geometries and microstructured materials, such as porous and cellular metals that are made up of ductile materials obeying nearly incompressible $J_2$ theory of plasticity.     

Robust closed-loop supply chain network design for perishable goods in agile manufacturing under uncertainty

In this study, a general comprehensive model is proposed for strategic closed-loop supply chain network design under interval data uncertainty. The proposed model considers various assumptions such as multiple periods, multiple products, and multiple supply chain echelons as well as uncertain demand and purchasing cost. In addition, bill of materials for each product is considered via a new approach in management of forward and reverse flows of products for producing new products and reusing or disassembling returned products. Uncertainty of parameters in the proposed model is handled via an interval robust optimisation technique. The model assumptions are well matched with decision making environments of food and high-tech electronics manufacturing industries. The factors that make these two industries similar are time-dependent properties of products such as prices and warehousing lifetime period. The computational results of solving the proposed model via LINGO 8 demonstrate efficiency of the proposed model in dealing with uncertainty in an agile manufacturing context.