Upstream Cutoff and Downstream Filters to Control of Seepage in Dams

The finite element method was used in this study to investigate cutoff walls and downstream filters to control seepage, the exit hydraulic gradient, and uplift forces for dams. Experimental data was used for validating the numerical modelling. The effective parameters are the length of filter and its distance downstream from the dam, the depth of the cutoff walls, the upstream dam head, and the thickness of alluvial foundation. The results show that by increasing filter length, the exit hydraulic gradient, uplift force, and seepage are reduced. The optimum relative length of the filter is L/H?=?0.028 which results in a decrease of about 65% in the exit hydraulic gradient, a 35% decrease in seepage and 10% reduction in the uplift force at the upstream foundation and a 60% decrease in the uplift force at the downstream foundation. Increase of cutoff wall depth reduces the exit hydraulic gradient, uplift force, and seepage. Using two cutoff walls both upstream and downstream of the dam decreases seepage, hydraulic gradient, and uplift force 132%, 450% and 11% respectively. However, using an upstream cutoff and downstream filter decreases seepage, hydraulic gradient, and uplift force by 180%, 490%, and 119% respectively. Thus, based on this study, recommendations for suitable combinations of upstream cutoff and downstream filter are provided.

     

Stochastic flow-shop scheduling with minimizing the expected number of tardy jobs

In this research, minimizing the expected number of tardy jobs in a dynamic m machine flow-shop scheduling problem, i.e., $ {F_m}\left| {{r_j}\left| {{\text{E}}\left[ {\sum {{U_j}} } \right]} \right.} \right. $ is investigated. It is assumed that the jobs with deterministic processing times and stochastic due dates arrive randomly to the flow-shop cell. The due date of each job is assumed to be normally distributed with known mean and variance. A dynamic method is proposed for this problem by which the m machine stochastic flow-shop problem is decomposed into m stochastic single-machine sub-problems. Then, each sub-problem is solved as an independent stochastic single-machine scheduling problem by a mathematical programming model. Comparison of the proposed method with the most effective rule of thumb for the proposed problem, i.e., shortest processing time first rule shows that the proposed method performs 23.9 % better than the SPT rule on average for industry-size scheduling problems.     

Modelling Energy Dissipation Over Stepped-gabion Weirs by Artificial Intelligence

The hydraulics of energy dissipation over stepped-gabion weirs is investigated by carrying out a series of laboratory experiments, building models to explain the experimental data, and testing their robustness by using the data reported by other researchers. The experiments comprise: six different stepped-gabion weirs tested in a horizontal laboratory flume, a wide range of discharge values, two weir slopes (V:H): 1:1 and 1:2, and gabion filling material gravel size (porosity equal to 38 %, 40 % and 42 %). These experimental setups were selected to ensure the development of both the nappe and skimming flow regimes within the measured dataset. The models developed for computing energy dissipation over stepped-gabion weirs comprise: multiple regression equations based on dimensional analysis theory, Artificial Neural Network (ANN) and Gene Expression Programming (GEP). The analysis shows that the measured data capture both flow regimes and the transition in between them and above all, and by using all of the data, it may be possible to identify the range of each regime. Energy dissipation modelled by the ANN formulation is successful and may be recommended for reliable estimates but those by GEP and regression analysis can still serve for rough-and-ready estimates in engineering applications.     

Observer-based reliable control for Lipschitz nonlinear networked control systems with quadratic protocol

International Journal of Control, Automation and Systems - In this paper, observer-based reliable control problem for nonlinear networked control systems (NCSs) with quadratic protocol (QP) in...     

Kinetic modeling and optimization of the operating condition of MTO process on SAPO-34 catalyst

In this paper, a new kinetic model for methanol to olefin process (MTO) over SAPO-34 catalyst was developed based on data obtained from a micro catalytic reactor using appropriate reaction network. The reaction rate equation has been introduced with consideration of reaction mechanism and the parameters were optimized on the experimental data by genetic algorithm. Comparing the experimental and predicted data showed that the predicted values from the presented model are well fitted to the experimental data. Using this kinetic model, the effect of most important operating conditions such as temperature, pressure, inlet water to methanol molar ratio and methanol space–time on the product distribution, has been examined. Finally, the optimal operating conditions for maximum production of the ethylene and the propylene were introduced.     

Fault detection and approximation for a class of linear impulsive systems using sliding‐mode observer

The objective of this paper is to develop a method for fault detection and approximation of linear impulsive systems exposed to actuator faults. Utilizing proposed sliding mode observer, states of the impulsive system are estimated. It is proved that estimation error dynamical system is asymptotically stable between jumps. In addition, an upper bound of the state estimation errors at jump instants has been derived explicitly. State estimations are used to reconstruct the fault signal. Furthermore, an upper bound of the fault estimation errors is obtained. Proficiency of the proposed method is evaluated under different fault scenarios using numerical simulations. Copyright © 2015 John Wiley & Sons, Ltd.     

Two-machine group scheduling problems in discrete parts manufacturing with sequence-dependent setups

This paper focuses on minimizing the total completion time in two-machine group scheduling problems with sequence-dependent setups that are typically found in discrete parts manufacturing. As the problem is characterized as strongly NP-hard, three search algorithms based on tabu search are developed for solving industry-size scheduling problems. Four different lower bounding mechanisms are developed to identify a lower bound for all problems attempted, and the largest of the four is aptly used in the evaluation of the percentage deviation of the search algorithms to assess their efficacy. The problem sizes are classified as small, medium and large, and to accommodate the variability that might exist in the sequence-dependent setup times on both machines, three different scenarios are considered. Such finer levels of classification have resulted in the generation of nine different categories of problem instances, thus facilitating the performance of a very detailed statistical experimental design to assess the efficacy and efficiency of the three search algorithms. The search algorithm based on long-term memory with maximal frequencies either recorded a statistically better makespan or one that is indifferent when compared with the other two with all three scenarios and problem sizes. Hence, it is recommended for solving the research problem. Under the three scenarios, the average percentage deviation for all sizes of problem instances solved has been remarkably low. In particular, a mathematical programming based lower bounding mechanism, which focuses on converting (reducing) the original sequence-dependent group scheduling problem with several jobs in each group to a sequence-dependent job scheduling problem, has served well in identifying a high quality lower bound for the original problem, making it possible to evaluate a lower average percentage deviation for the search algorithm. Also, a 16–17-fold reduction in average computation time for solving a large problem instance with the recommended search algorithm compared with identifying just the lower bound of (not solving) the same instance by the mathematical programming based mechanism speaks strongly in favor of the search algorithm for solving industry-size group scheduling problems.     

A robust and high-performance queue management controller for large round trip time networks

Congestion management for transmission control protocol is of utmost importance to prevent packet loss within a network. This necessitates strategies for active queue management. The most applied active queue management strategies have their inherent disadvantages which lead to suboptimal performance and even instability in the case of large round trip time and/or external disturbance. This paper presents an internal model control robust queue management scheme with two degrees of freedom in order to restrict the undesired effects of large and small round trip time and parameter variations in the queue management. Conventional approaches such as proportional integral and random early detection procedures lead to unstable behaviour due to large delay. Moreover, internal model control–Smith scheme suffers from large oscillations due to the large round trip time. On the other hand, other schemes such as internal model control–proportional integral and derivative show excessive sluggish performance for small round trip time values. To overcome these shortcomings, we introduce a system entailing two individual controllers for queue management and disturbance rejection, simultaneously. Simulation results based on Matlab/Simulink and also Network Simulator 2 (NS2) demonstrate the effectiveness of the procedure and verify the analytical approach.     

Makespan minimization of a flowshop sequence-dependent group scheduling problem

The flowshop sequence dependent group scheduling problem with minimization of makespan as the objective (F _m |fmls, S _plk, prmu|C _max ) is considered in this paper. It is assumed that several groups with different number of jobs are assigned to a flow shop cell that has m machines. The goal is to find the best sequence of processing the jobs in each group and the groups themselves with minimization of makespan as the objective. A mathematical model for the research problem is developed in this paper. As the research problem is shown to be NP-hard, a hybrid ant colony optimization (HACO) algorithm is developed to solve the problem. A lower bounding technique based on relaxing a few constraints of the mathematical model developed for the original problem is proposed to evaluate the quality of the HACO algorithm. Three different problem structures, with two, three, and six machines, are used in the generation of the test problems to test the performance of the algorithm and the lower bounding technique developed. The results obtained from the HACO algorithm and those that have appeared in the published literature are also compared. The comparative results show that the HACO algorithm has a superior performance compared to the best available algorithm based on memetic algorithm with an average percentage deviation of around 1.0% from the lower bound.     

Real-time PCR systems for the detection of the gluten-containing cereals wheat,spelt, kamut,rye, barley and oat

Real-time PCR assays, using TaqMan^® probes, were applied to detect the gluten-containing cereals. Homologues target sequences encoding high molecular weight (HMW) glutenin were chosen to detect wheat, kamut, spelt and rye. For detecting barley, the gene Hor3 was selected. For the detection of oat the gene encoding the 12S seed storage protein was chosen. Based on this sequence data, primer and probe sequences were generated for the real-time PCR. A plant specific primer probe system based on 18S rRNA gene was chosen to detect amplificability of the extracted nucleic acids. The specificity of the primer and probe systems was checked using different lines from different origins of the species to be detected. The HMW glutenin system is specific for the corresponding species, as are the systems for the barley Hor3 gene and the oat 12S seed storage protein. The sensitivity of the systems was determined testing different matrices. With the HMW glutenin system 2.5 mg/kg of wheat in vegetable food matrices and 5 mg/kg of wheat in meat products were detected. The oat and the barley specific systems resulted in a sensitivity of 10 mg/kg. The detection method showed a satisfactory ruggedness.