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.     

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.     

Divided-wall distillation column design using molecular tracking

Divided-wall column (DWC) is an intensified separation process and so far developing a simple procedure for designing these units has been challenging. In this work, the concept of molecular tracking has been integrated with conventional methods to build a simple and easy-to-use methodology for designing DWCs for multicomponent separations. Application of the proposed approach is highlighted through several three- and four-component mixtures. The configuration obtained using molecular tracking gives a design with lower energy demands for the column reboiler, compared to other design methodology, which directly impacts the OPEX of the system.     

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.

     

Molecular tracking: A concept for side-draw distillation column design

A simple approach is introduced to locate a side-draw tray for ternary and multi-component mixtures with middle boiling component(s) present in the system at trace levels. The concept is based on a probability function defined by the thermodynamic properties of the system. The advantage of this method over existing methods is the ability to quickly and efficiently provide a feasible configuration of the distillation unit without relying on rigorous optimization or trial and error approaches. Moreover, it provides an intuitive understanding of the movements of the middle boiling components in the column.     

Integrated design and control of reactive distillation processes using the driving force approach

Superior controllability of reactive distillation (RD) systems, designed at the maximum driving force (design-control solution) is demonstrated in this article. Binary or multielement single or double feed RD systems are considered. Reactive phase equilibrium data, needed for driving force analysis and design of the RD system, is generated through an in-house property prediction tool. Rigorous steady-state simulation is carried out in ASPEN plus in order to verify that the predefined design targets and dynamics are met. A multiobjective performance function is employed to evaluate the performance of the RD system in terms of energy consumption, sustainability metrics (total CO₂ footprint), and control performance. Controllability of the designed system is evaluated using indices like the relative gain array (RGA) and Niederlinski index (N_I ), to evaluate the degree of loop interaction, as well as through dynamic simulations using proportional-integral (PI) controllers and model predictive controllers (MPC). The design-control of the RD systems corresponding to other alternative designs that do not take advantage of the maximum driving force is also investigated. The analysis shows that the RD designs at the maximum driving force exhibit enhanced controllability and lower carbon footprint than the alternative RD designs.