Fuzzy logic modeling of Pb (II) sorption onto mesoporous NiO/ZnCl2-Rosa Canina-L seeds activated carbon nanocomposite prepared by ultrasound-assisted co-precipitation technique

In this study, NiO/Rosa Canina-L seeds activated carbon nanocomposite (NiO/ACNC) was prepared by adding dropwise NaOH solution (2 mol/L) to raise the suspension pH to around 9 at room temperature under ultrasonic irradiation (200 W) as an efficient method and characterized by FE-SEM, FTIR and N2 adsorption-desorption isotherm. The effect of different parameters such as contact time (0–120 min), initial metal ion concentration (25–200 mg/L), temperature (298, 318 and 333 K), amount of adsorbent (0.002–0.007 g) and the solution's initial pH (1–7) on the adsorption of Pb (II) was investigated in batch-scale experiments. The equilibrium data were well fitted by Langmuir model type 1 (R2 > 0.99). The maximum monolayer adsorption capacity (qm) of NiO/ACNC was 1428.57 mg/L. Thermodynamic parameters (¿G°, ¿H° and ¿S°) were also calculated. The results showed that the adsorption of Pb (II) onto NiO/ACNC was feasible, spontaneous and exothermic under studied conditions. In addition, a fuzzy-logic-based model including multiple inputs and one output was developed to predict the removal efficiency of Pb (II) from aqueous solution. Four input variables including pH, contact time (min), dosage (g) and initial concentration of Pb (II) were fuzzified using an artificial intelligence-based approach. The fuzzy subsets consisted of triangular membership functions with eight levels and a total of 26 rules in the IF-THEN approach which was implemented on a Mamdani-type of fuzzy inference system. Fuzzy data exhibited small deviation with satisfactory coefficient of determination (R2 > 0.98) that clearly proved very good performance of fuzzy-logic-based model in prediction of removal efficiency of Pb (II). It was confirmed that NiO/ACNC had a great potential as a novel adsorbent to remove Pb (II) from aqueous solution.Postprint (author's final draft

Design of Fuzzy Controllers for Embedded Systems With JFML

Fuzzy rule-based systems (FRBSs) have been successfully applied to a wide range of real-world problems. However, they suffer from some design issues related to the difficulty to implement them on different hardware platforms without additional efforts. To bridge this gap, recently, the IEEE Computational Intelligence Society has sponsored the publication of the standard IEEE Std 1855-2016 which is aimed at providing the fuzzy community with a well-defined approach to model FRBSs in a hardwareindependent way. In order to provide a runnable version of an FRBS that is designed in accordance with the IEEE Std 1855-2016, the open source library Java Fuzzy Markup Language (JFML) has been developed. However, due to hardware and/or software limitations of embedded systems, it is not always possible to run an IEEE Std 1855-2016 FRBS on this kind of systems. The aim of this paper is to overcome this drawback by developing a new JFML module that assists developers in the design and implementation of FRBSs for open hardware–embedded systems. In detail, the module supports several connection types (WiFi, Bluetooth, and USB) in order to make feasible running FRBSs in a remote computer when, due to hardware limitations, it is not possible that they run locally in the embedded systems. The new JFML module is ready for ArduinoTM and Raspberry Pi, but it can be easily extended to other hardware architectures. Moreover, the new JFML module allows to automatically generate runnable files on ArduinoTM or Raspberry Pi in order to support nonexpert users, that is, users without specific knowledge about embedded systems or without strong programming skills. The use of the new JFML module is illustrated in two case studies.This paper has been supported in part by the Spanish Ministry of Economy and Competitiveness (Projects TIN2017-89517-P, TIN2015-68454-R, TIN2017-84796-C2-1-R, and TIN2017-90773-REDT) and the Andalusian Government. In addition, Jose M. Alonso is Ramon y Cajal Researcher (RYC-2016-19802). Financial support from the Galician Ministry of Education (grants ED431F 2018/02, GRC2014/030 and accreditation 2016-2019, ED431G/08), co-funded by the European Regional Development Fund (ERDF/FEDER program), is also gratefully acknowledged

Mitigation of environmental hazards of sulfide mineral flotation with an insight into froth stability and flotation performance

Today\u27s major challenges facing the flotation of sulfide minerals involve constant variability in the ore composition; environmental concerns; water scarcity and inefficient plant performance. The present work addresses these challenges faced by the flotation process of complex sulfide ore of Mississippi Valley type with an insight into the froth stability and the flotation performance. The first project in this study was aimed at finding the optimum conditions for the bulk flotation of galena (PbS) and chalcopyrite (CuFeS₂) through Response Surface Methodology (RSM). In the second project, an attempt was made to replace toxic sodium cyanide (NaCN) with the biodegradable chitosan polymer as pyrite depressant. To achieve an optimum flotation performance and froth stability, the third project utilized two types of nanoparticles; silica (SiO₂) and alumina (Al₂O₃) as process aids. The fourth project investigated the impact of water chemistry on the process outcomes in an attempt to replace fresh water with sea water. In the last project, five artificial intelligence (AI) and machine learning (ML) models were employed to model the flotation performance of the ore which will allow the building of intelligent systems that can be used to predict the process outcomes of polymetallic sulfides. It was concluded that chitosan can be successfully used as a biodegradable depressant. Alumina nanoparticles successfully enhanced both froth stability and flotation performance while silica nanoparticles did not. Seawater had a negative effect on both the froth stability and the grade of lead (Pb) and copper (Cu) but it improved the recoveries of both Pb and Cu minerals. Hybrid Neural Fuzzy Interference System (HyFIS) ML model showed the best accuracy to be adopted for automated sulfide ore flotation process in the future --Abstract, page iii

Ultrasonic Spot Welding of Dissimilar Metal Sheets: An Experimental, Numerical and Metallurgical Investigation

Ultrasonic metal welding (USMW) is a new and emerging concept used in the industries over the past twenty years and serving to the manufacturing sectors like aviation, medical, microelectronics, automotive and much more due to various hurdles faced by conventional fusion welding process. USMW is a clean and reliable technique in which the welding takes place with a high energy, no flux or filler metal needed, longer tool life and it takes very short time (less than one second) to weld materials in a perfect controllable environment with greater efficiency.To acquire high vibration amplitude in USMW, there is a necessity to design a welding system that consists of components like a booster and horn. The principal purpose of these parts is to amplify the input amplitude of vibration so that the energy transferred to the welding spot should be sufficient for creating a joint. In the present study, new type of booster and horn are proposed and modelled with adequate precision not only to produce high-quality welds but also to solve a lot of issues faced while designing these types of ultrasonic tools. The modal analysis module of finite element method (FEM) is used to analyze the effects of different step lengths and fillet radius on its natural frequency of 20 kHz, ensuring that these components will be in a resonating condition with other parts of the system. It is found that there were 1.11 % and 2.52 % errors in the length calculation of both parts. Similarly, 0.61 % error is obtained for both while calculating the magnification ratio. However, such low levels of errors may be considered to be insignificant. The dynamic analysis has also been performed to find out the stress distribution in both parts under cyclic loading conditions. Due to these cyclic loading conditions, the nodal regions (hot areas) are under highly stressed, and the relevant temperature field is consequently determined. The results obtained from the simulation, and experimental results were found to be close to each other and an error of 2% was noticed. Other welding components are also fabricated such as anvil, specimen-holder and backing plate for producing a satisfactory weld. Meanwhile, the complex mechanism behind the USMW has been addressed and modelled analytically. This model can predict the forces as well as temperatures those occur during the welding process and also explains the effects of various material properties and surface conditions on the weld behaviour. The experiments have been performed on the aluminium, copper, brass and stainless steel metal sheets with a number of different configurations, anvil designs, and surface conditions. The fundamental aspect of this study is to control the process parameters like vibration amplitude, weld pressure and weld time so that, an appreciable weld strength can be obtained. Thus, tensile shear and T-peel failure load studies suggest that increase in vibration amplitude means the increase of scrubbing action between the faying surfaces, resulting a better bonding strength. Similarly, increase in weld pressure also increases these weld failure loads and reach a peak value at a particular pressure. But, subsequently, these failure loads decrease due to suppression of relative motion between sheets and initiation of cracks. Excessive weld time also causes cracks around the weld spot. Likewise, if the thickness of the sheets increased, weld strengths are also increased due to absorption of more amount of ultrasonic energy. Moreover, the highest weld interface temperatures and weld areas are observed at the end of weld time because of the larger plastic deformation at the mating surfaces. For all the experiments, first anvil design shows maximum failure loads due to its non-cutting width and angle of knurls. Likewise, on the increase of surface roughness, the tensile shear, and T-peel failure loads decrease. It is found that, in lubricating condition, the highest failure loads are obtained. Furthermore, the polynomial regression, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methods are developed and compared for each performance measure so that the whole welding process can be accurately described by a best predictive model. A welding mechanics based numerical model has been developed which can predict the temperatures during USMW process for various surface conditions. For all the experimental investigations, the predictive results show good agreement with the experimental values. In addition to it, acoustic softening during ultrasonic welding is found to very significant for the reduction in yield strength of the weld material up to 95 %. It is seen that the quality of welding depends on the material properties, process parameters, and thickness of the workpiece. The present investigation also explains in details the effect of process parameters on the responses through metallurgical analysis. A quality lobe of welding like “under weld”, “good weld” and “over weld” is proposed after observing the fractured samples in optical microscopy and scanning electron microscopy (SEM). Meantime, energy dispersive spectroscopy (EDS) and X- ray diffraction (XRD) analyses are also used to reveal the thickness of interatomic diffusion and IMCs along the weld interface

NONLINEAR IDENTIFICATION AND CONTROL: A PRACTICAL SOLUTION AND ITS APPLICATION

It is well known that typical welding processes such as laser welding are nonlinear although mostly they are treated as linear system. For the purpose of automatic control, Identification of nonlinear system, especially welding processes is a necessary and fundamental problem. The purpose of this research is to develop a simple and practical identification and control for welding processes. Many investigations have shown the possibility to represent physical processes by nonlinear models, such as Hammerstein structure, consisting of a nonlinearity and linear dynamics in series with each other. Motivated by the fact that typical welding processes do not have non-zeroes, a novel two-step nonlinear Hammerstein identification method is proposed for laser welding processes. The method can be realized both in continuous and discrete case. To study the relation among parameters influencing laser processing, a standard diode laser processing system is built as system prototype. Based on experimental study, a SISO and 2ISO nonlinear Hammerstein model structure are developed to approximate the diode laser welding process. Specific persistent excitation signals such as PRTS (Pseudo-random-ternary-series) to Step signal are used for identification. The model takes welding speed as input and the top surface molten weld pool width as output. A vision based sensor implemented with a Pulse-controlled-CCD camera is proposed and applied to acquire the images and the geometric data of the weld pool. The estimated model is then verified by comparing the simulation and experimental measurement. The verification shows that the model is reasonably correct and can be use to model the nonlinear process for further study. The two-step nonlinear identification method is proved valid and applicable to traditional welding processes and similar manufacturing processes. Based on the identified model, nonlinear control algorithms are also studied. Algorithms include simple linearization and backstepping based robust adaptive control algorithm are proposed and simulated

Measurements, Models, Systems and Design

531 s.

Induction Motors

AC motors play a major role in modern industrial applications. Squirrel-cage induction motors (SCIMs) are probably the most frequently used when compared to other AC motors because of their low cost, ruggedness, and low maintenance. The material presented in this book is organized into four sections, covering the applications and structural properties of induction motors (IMs), fault detection and diagnostics, control strategies, and the more recently developed topology based on the multiphase (more than three phases) induction motors. This material should be of specific interest to engineers and researchers who are engaged in the modeling, design, and implementation of control algorithms applied to induction motors and, more generally, to readers broadly interested in nonlinear control, health condition monitoring, and fault diagnosis

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included