Residual generation with unknown input observer for linear systems in the presence of unmatched uncertainties

Journal of Mechanical Science and Technology - In this paper, we deal with residual vector generation for fault detection problems in linear systems via unknown input observer (UIO) when the...     

Cadmium Selective PVC-Membranes Sensor Based on 1, 2-Bis (Quinoline-2-Carboxamido) -4-Chlorobenzene as a Neutral Carrier

The 1, 2-bis(quinoline-2-Carboxamido)-4-chlorobenzene (H₂Clbqb) was used as an excellent ionophore in the construction of a cadmium(II)-selective PVC-based membrane sensor. The influences of membrane compositions on the potentiometric response of the electrodes have been found to substantially improve the performance characteristics. The best performance was obtained with a membrane composition of (H₂Clbqb) (5%): PVC (31%): DBP (61%): NaTPB (3%). The sensor shows a Nernestian response for cadmium ions over a wide concentration range (1.0times10⁶ to 1.0 times 10^-1 mol.L^-1) with slope of 30.3 plusmn0.4 mV decade¹. The limit of detection was 8.0 times10^-7 mol.L^-1. It illustrates a relatively fast response time in the whole concentration range (< 10 s) and it can be used for at least 8 weeks without any divergence in potential. The electrode can be used in the pH range from 2.4 to 9.0. The selectivity coefficient of some alkali, alkaline earth, transition and heavy metal ions towards Cd²⁺ion have been determined. The results show proposed Cd sensor is selective over a number of mono, bi- and trivalent cations such as Pb²⁺, Fe³⁺, Ni²⁺, K⁺, and Mg²⁺. The analytical usefulness of the proposed electrode has been evaluated by its application in the determination of cadmium in real samples. It was successfully applied for the direct determination of Cd²⁺ in standard and real sample solutions.     

Model tree approach for predicting uniaxial compressive strength and Young’s modulus of carbonate rocks

Bulletin of Engineering Geology and the Environment - The uniaxial compressive strength (UCS) and Young’s modulus (E) of rock are important parameters for evaluating the strength,...     

Modelling of multi-period multi-product production planning considering production routes

The multi-period multi-product (MPMP) production planning problems, generally, deal with matching production levels of individual products with fluctuated demands over planning horizon. The conventional MPMP optimisation models suffer from insufficient utilisation of available capacity of machines. This fallacy is due to inappropriate formulation of machine capacity and material handling constraints. In this study, a novel mathematical model is proposed to simultaneously optimise production quantities and provide information about managerial decisions such as subcontracting, carrying inventory/backordering, and also hiring/layoff personnel. The problem is then formulated as a mixed integer linear programming (MILP) model by applying appropriate linearisation of non-linear components. The objective is to minimise production costs comprising of production, storage, shortage, subcontracting costs and costs associated with hiring/dismissing labourers. Superiority of the proposed model over existing ones, has been initially evaluated by solving the case presented by Byrne and Bakir [Byrne, M.D. and Bakir, M.A., 1999. Production planning using a hybrid simulation-analytical approach. International Journal of Production Economics, 59 (1), 305–311], and then evaluated by comparing the results obtained from solving both the proposed and the conventional MPMP production planning models using a 100-randomly-generated-test-problem.     

A new hybrid ANFIS–PSO model for prediction of peak particle velocity due to bench blasting

In this paper, a novel hybrid approach is proposed for predicting peak particle velocity (PPV) due to bench blasting in open pit mines. The proposed approach is based on the combination of adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO). In this approach, the PSO is used to improve the performance of ANFIS. Furthermore, a model is developed based on support vector regression (SVR) approach. The models are trained and tested based on actual data compiled from 120 blast rounds in Sarcheshmeh copper mine. To determine the accuracy and efficiency of ANFIS–PSO and SVR models, a statistical model (USBM equation) is applied. According to the obtained results, both techniques can be used to predict the PPV, but the comparison of models shows that the ANFIS–PSO model provides better results. Root mean square error (RMSE), variance account for (VAF), and coefficient of determination (R ²) indices were obtained as 1.83, 93.37 and 0.957 for ANFIS–PSO model, respectively.     

Synthesis and characterisation of nanostructured hardystonite coating on stainless steel for biomedical application

Here, nanostructured hardystonite bioceramic (Ca₂ ZnSi₂ O₇) was synthesised from tetraethyl orthosilicate, zinc nitrate hexahydrate, and calcium nitrate tetrahydrate via sol–gel method, dried at 60–120°C, and finally calcinated at 1300°C. X‐ray diffraction (XRD) analysis confirmed the formation of hardystonite bioceramic. Afterwards, electrophoretic method was utilised to coat the hardystonite ceramic on 316L stainless steel (SS). Methanol solution was used as suspension solvent. The best deposition procedure was carried out by electrophoretic device in the voltage of 50 V for 5 min. XRD analysis was employed for phase characterisation and scanning electron microscopy was utilised for microstructural and morphological characterisations of the coatings. Chemical composition of the coating was evaluated by energy‐dispersive X‐ray spectroscopy. The hardystonite coating improved the corrosion resistance of the substrate, so the corrosion current density in the coated samples was less than the uncoated ones (nine times). In order to assess the bioactivity of the coating, simulated body fluid was used. The main results of the coated sample bioactivity demonstrated that the nanostructured hardystonite coating could amend the in vitro SS bioactivity. Therefore, SS coated with nanostructured hardystonite may be a promising candidate to be applied as bioactive hard tissue implants.Inspec keywords: bioceramics, stainless steel, X‐ray diffraction, corrosion protective coatings, X‐ray chemical analysis, sol‐gel processing, calcium compounds, current density, nanofabrication, zinc compounds, scanning electron microscopy, corrosion resistance, calcination, crystal microstructure, nanostructured materials, prosthetics, nanomedicine, electrophoretic coatings, electrophoretic coating techniquesOther keywords: X‐ray diffraction analysis, electrophoretic method, XRD analysis, phase characterisation, microstructural characterisations, morphological characterisations, energy‐dispersive X‐ray spectroscopy, coated sample bioactivity, nanostructured hardystonite coating, zinc nitrate hexahydrate, sol–gel method, 316L stainless steel, tetraethyl orthosilicate, calcium nitrate tetrahydrate, suspension solvent, deposition procedure, scanning electron microscopy, chemical composition, corrosion resistance, corrosion current density, bioactive hard tissue implants, temperature 1300.0 degC, voltage 50.0 V, time 5.0 min, temperature 60 degC to 120 degC, Ca₂ ZnSi₂ O₇      

Minimum positive influence dominating set and its application in influence maximization: a learning automata approach

In recent years, with the rapid development of online social networks, an enormous amount of information has been generated and diffused by human interactions through online social networks. The availability of information diffused by users of online social networks has facilitated the investigation of information diffusion and influence maximization. In this paper, we focus on the influence maximization problem in social networks, which refers to the identification of a small subset of target nodes for maximizing the spread of influence under a given diffusion model. We first propose a learning automaton-based algorithm for solving the minimum positive influence dominating set (MPIDS) problem, and then use the MPIDS for influence maximization in online social networks. We also prove that by proper choice of the parameters of the algorithm, the probability of finding the MPIDS can be made as close to unity as possible. Experimental simulations on real and synthetic networks confirm the superiority of the algorithm for finding the MPIDS Experimental results also show that finding initial target seeds for influence maximization using the MPIDS outperforms well-known existing algorithms.     

Developing a mathematical assessment model for blasting patterns management： Sungun copper mine

Blasting is one of the most important operations in the mining projects that has effective role in the whole operation physically and economically. Unsuitable blasting pattern may lead to unwanted events such as poor fragmentation, back break and fly rock. Multi attribute decision making（MADM） can be useful method for selecting the most appropriate blasting pattern among previously performed patterns. In this work, initially, from various already performed patterns, efficient and inefficient patterns are determined using data envelopment analysis（DEA）. In the second step, after weighting impressive attributes using experts＇ opinion, elimination Et choice translating reality（ELECTRE） was used for ranking the efficient patterns and recognizing the most appropriate pattern in the Sungun Copper Mine, Iran. According to the obtained results, blasting pattern with the hole diameter of 15.24 cm, burden of 3 m, spacing of 4 m and stemming of 3.2 m has selected as the best pattern and has selected for future operation.     

Designing a control mechanism using earned value analysis: an application to production environment

Earned value analysis is a project performance method which simultaneously presents both cost and schedule performances. The purpose of this paper is to model the uncertainty associated with activity duration in earned value analysis. The approach incorporates to design a control mechanism, which would be applicable through production control as well as project management problems. The job processing times have been considered as triangular fuzzy number. Costs are assumed to be directly related to fuzzy activity time, which are estimated through a bottom up hierarchy process. Consequently, different earned value metrics have been achieved. Research findings provide an efficient control mechanism in earned value analysis, which would be highly applicable in production control area. This research also yields a novel approach for representing a production performance index during implementation of production processes. In addition to the above mentioned issues, forecasting features can be further performed for predicting completion time of products for delivery to the customer. The approach presented in this paper has been successfully implemented through a multi-period–multi-product production planning problems, which efficiently demonstrates the applicability of the proposed control mechanism.     

Determining the optimum cutting direction in granite quarries through experimental studies: a case study of a granite quarry

Bulletin of Engineering Geology and the Environment - Optimization of cutting operations in quarrying and processing of building stones leads to certain reductions in operational costs. Despite the...