Ion-crosslinked carboxymethyl cellulose/polyaniline bio-conducting interpenetrated polymer network: preparation,characterization and application for an efficient removal of Cr(VI) from aqueous solution

A simple, cheap, and environmentally friendly bio-conducting interpenetrated polymer blend network was prepared and introduced as a highly efficient system with suitable physical and mechanical properties for industrial removal of toxic Cr(VI) ions from aqueous solution. Carboxymethyl cellulose/polyaniline (CMC/PANI) interpenetrated network (IPN) blend was prepared by simple simultaneous ion-cross-linking of CMC and PANI chains using Al³⁺ cations. The CMC/PANI bio-conducting nanocomposite was characterized using X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), and scanning electron microscopy equipped with an "energy dispersive X-ray spectroscopy" (SEM–EDX) technique. The CMC/PANI blend, ion-cross-linked by Al³⁺ cations, showed good stability and high surface area, proper for the removal of toxic Cr(VI) ions of the aqueous solution. Batch removal experiments were accomplished and the impression of effective variables including solution pH, initial concentration of Cr(VI) ions, contact time, and adsorbent dosage were checked and optimized. The outcome of our findings revealed that the removal of Cr(VI) ions by CMC/PANI nanocomposite IPN strongly depends on solution pH. The removal information was matched with the Langmuir adsorption isotherm model and the utmost monolayer adsorption capacity at pH 2 was 136.98 mg/g at 25 °C. The pseudo-second-order kinetics were operated and the thermodynamic parameters suggested spontaneous and exothermic nature of the adsorption process. Consequences indicated that CMC/PANI nanocomposite IPN could be an affective eco/environmentally friendly adsorbent for the removal of Cr(VI) ions from aqueous solutions.

     

A hybrid fuzzy multiple criteria group decision making approach for sustainable project selection

In this paper, a new hybrid fuzzy multiple criteria group decision making (FMCGDM) approach has been proposed for sustainable project selection. First, a comprehensive framework, including economic, social, and environmental effects of an investment, strategic alliance, organizational readiness, and risk of investment has been proposed for sustainable project selection. As the relative importance of the criteria of the proposed framework are hard to find through several conflictive preferences of a group of Decision Makers (DMs) so, a goal programming (GP) has been supplied to this aim considering multiplicative and fuzzy preference relation. Then, a fuzzy TOPSIS method has been developed to assess the fitness of investment chances. It is based on Preference Ratio (PR), which is known as an efficient ranking method for fuzzy numbers, and a fuzzy distance measurement. The properties of proposed hybrid approach make it robust for modeling real case of uncertain group decision making problems. The FMCGDM has been developed through a linkage between Lingo 11.0, MS-Excel 12.0, and Visual Basic 6.0. The proposed hybrid approach has been applied in a real case study called Iranian financial and credit institute for sustainable project selection.     

Adaptive wavenet controller design for teleoperation systems with variable time delays using singular perturbation method

The main goal of controller design in teleoperation systems is to achieve stability and optimal operation in presence of factors such as time delays, system disturbances and modeling errors. This paper proposes a new method of controller design based on wavenet with singular perturbation method for the bilateral teleoperation of robots through the internet. The wavenet controller could overcome the variable time delay in teleoperation system. This new method introduces a reduced-order structure for control and stability of teleoperation systems. By using singular perturbation method, teleoperation system is decomposed into two fast and slow subsystems. This method is a step towards reduced-order modeling. In this method, we use a feedback linearization method in master subsystem and a wavenet controller for slave subsystem. In wavenet controller, we used a learning method so that the system was Lyapunov stable. As the stability of the model is highly dependent on the learning of the system, we use Lyapunov stability in this method. It has been tried to reduce the tracking error between the master and the slave subsystems. In this structure the position of master-slave are compared together and controlling signal is applied to the slave so that they can track each other in the least possible time. In all schemes the effectiveness of the system is shown through the simulations and they have been compared with each other.     

Weighted and constrained possibilistic C-means clustering for online fault detection and isolation

In this paper, a new weighted and constrained possibilistic C-means clustering algorithm is proposed for process fault detection and diagnosis (FDI) in offline and online modes for both already known and novel faults. A possibilistic clustering based approach is utilized here to address some of the deficiencies of the fuzzy C-means (FCM) algorithm leading to more consistent results in the context of the FDI tasks by relaxing the probabilistic condition in FCM cost function. The proposed algorithm clusters the historical data set into C different dense regions without having precise knowledge about the number of the faults in the data set. The algorithm incorporates simultaneously possibilistic algorithm and local attribute weighting for time-series segmentation. This allows different weights to be allocated to different features responsible for the distinguished process faults which is an essential characteristic of proper FDI operations. A set of comparative studies have been carried out on the large-scale Tennessee Eastman industrial challenge problem and the DAMADICS actuator benchmark to demonstrate the superiority of the proposed algorithm in process FDI applications with respect to some available alternative approaches.     

Maximal covering location problem (MCLP) with fuzzy travel times

This paper presents a fuzzy maximal covering location problem (FMCLP) in which travel time between any pair of nodes is considered to be a fuzzy variable. A fuzzy expected value maximization model is designed for such a problem. Moreover, a hybrid algorithm of fuzzy simulation and simulated annealing (SA) is used to solve FMCLP. Some numerical examples are presented, solved and analyzed to show the performance of the proposed algorithm. The results show that the proposed SA finds solutions with objective values no worse than 1.35% below the optimal solution. Furthermore, the simulation-embedded simulated annealing is robust in finding solutions.     

A modular Decision Support System for optimum investment selection in presence of uncertainty: Combination of fuzzy mathematical programming and fuzzy rule based system

In this paper, we have developed a modular Decision Support System (DSS) in order to select an optimum portfolio of several chances for investments in presence of uncertainty. The investments are considered as the projects so as their initial investment costs, profits, resource requirement, and total available budget are assumed to be uncertain. This uncertainty has been modeled using fuzzy concepts. The proposed DSS has two main modules. The first one is a fuzzy binary programming model which represents the mathematical model of the associated fuzzy capital-budgeting problem. It involves finding optimum combination of investment portfolio considering a multi-objective measurement function and subject to several set of constraints. The results of optimistic and pessimistic analysis of the aforementioned fuzzy binary programming model plus a managerial Confidence Level (CL) value are treated as input of a fuzzy rule based system which is the second module of the proposed DSS. Although some projects are simple to make a decision about at the final step of the first module but the unique output of the second module of the proposed DSS is Risk of Investment (ROI) for all remained project. The logic relations between precedence parts of the rules as well as CL value will work in favor of computational efforts in second module through diminishing some unessential rules. This will help to define a complete set of fuzzy IF-THEN rules more efficiently. The proposed DSS can help the decision makers to select an optimum investment portfolio with minimum risk in a complete ambiguous condition.     

Wavelet neural adaptive proportional plus conventional integral-derivative controller design of SSSC for transient stability improvement

Although the PI or PID (PI/PID) controllers have many advantages, their control performance may be degraded when the controlled object is highly nonlinear and uncertain; the main problem is related to static nature of fixed-gain PI/PID controllers. This work aims to propose a wavelet neural adaptive proportional plus conventional integral-derivative (WNAP+ID) controller to solve the PI/PID controller problems. To create an adaptive nature for PI/PID controller and for online processing of the error signal, this work subtly employs a one to one offline trained self-recurrent wavelet neural network as a processing unit (SRWNN-PU) in series connection with the fixed-proportional gain of conventional PI/PID controller. Offline training of the SRWNN-PU can be performed with any virtual training samples, independent of plant data, and it is thus possible to use a generalized SRWNN-PU for any systems. Employing a SRWNN-identifier (SRWNNI), the SRWNN-PU parameters are then updated online to process the error signal and minimize a control cost function in real-time operation. Although the proposed WNAP+ID is not limited to power system applications, it is used as supplementary damping controller of static synchronous series compensator (SSSC) of two SSSC-aided power systems to enhance the transient stability. The nonlinear time-domain simulation and system performance characteristics in terms of ITAE revealed that the WNAP+ID has more control proficiency in comparison to PID controller. As additional simulations, the features of the proposed controller are compared to those of the literature while some of its promising features like its fast noise-rejection ability and its high online adapting ability are also highlighted.     

Design of actively-cooled microvascular materials: a genetic algorithm inspired network optimization

Structural and Multidisciplinary Optimization - The design of a microvascular flow network embedded in an actively-cooled polymeric material is presented. A multi-objective Genetic Algorithm (GA)...     

Morphological optimization of electrospun polyacrylamide/MWCNTs nanocomposite nanofibers using Taguchi’s experimental design

The morphological characteristic of electrospun polyacrylamide/multi-walled carbon nanotube (PAAm/MWCNTs) nanocomposite nanofibers is optimized in this work using Taguchi’s experimental design. The optimization is performed considering the effect of PAAm concentration, MWCNTs content, flow rate, and applied voltage on average nanofibers diameter. The reasonable dispersion of MWCNTs in PAAm solution is first ascertained via optical microscopy method. The experimental data required for the optimization process are then provided by statistical calculations on field-emission scanning electron microscopy images of the samples formulated based on a designed L ₉ orthogonal array. PAAm concentration is found to have the most contribution on final fibers morphology according to the results obtained from simultaneous implementation of the analysis of variance and mean effect assessment. Therefore, PAAm concentration, which is in consistence with solution viscosity and surface tension parameter, is found to have the most contribution to forming nanofibers including the finest fiber diameter. On the contrary, the flow rate of solution among the selected parameters shows the least effect on average nanofiber diameter.     

A patient-specific multibody kinematic model for representation of the scoliotic spine movement in frontal plane of the human body

Multibody models of scoliotic spine have shown great promise in planning scoliosis surgery by providing predictive information concerning the surgery outcome. To provide good predictive information, it is important that the kinematic models underlying the movement of the spine models would be personalized to give good estimates of the spine in different positions, which is lacking in the existing literature. This paper aims to develop a patient-specific multibody kinematic model of the scoliotic spine to represent its movement in frontal plane of the human body. The model is an open-chain mechanism comprising rigid links interconnected with rotary joints. To represent the movement, the mechanism lays on the spine curve and estimates the curve and the location and orientation of vertebrae. To personalize the mechanism for a patient, a minimization problem is defined to give the number of the links and their length by using X-rays of different spine positions. The feasibility and capabilities of our patient-specific model are tested by using the data from preoperative X-rays of five positions of 10 AIS (adolescent idiopathic scoliosis) patients; three of the X-rays are routine in scoliosis standard care. The mechanism is personalized to each patient by using the three routine X-rays, and it is used to estimate all the five positions. Root-mean-square-errors (RMSE) of the curve, location, and orientation are 2e–5 mm, 0.27 mm, and 0.25°, respectively. The small RMSEs imply that our kinematic model is capable of estimating the scoliotic spine positions in the frontal plane and thus of describing the scoliotic spine movement in this plane. Our personalization using X-rays of three spine positions helps to set better values for the kinematic parameters (such as the length of the links) for more accurate estimates of the spine in the frontal plane.