A new characterization of atomic stable parts for a partial order relation applied to the one-machine scheduling problem

A set E endowed with a partial order relation Ɍ can be decomposed into subsets called “atomic stable parts” for Ɍ, totally ordered. These atomic stable parts are the equivalence classes of an equivalence relation T (®) [1]. In fact if S(x) is the atom containing x (x Є E) and E endowed with the partial order relation Ɍ), then Cl(x) is the equivalence class of x for the equivalence relation T (®) defined by: x,y)Є E 2 , x® y not (x Ɍ y or y Ɍ x); (® is a symmetric relation by construction. Its transitive closure T (®) is an equivalence relation [2]. In this article we propose a new characterization of the atomic stable parts for Ɍ. The approach consists in defining a square matrix B called matrix of “Ranks” from the relation Ɍ whose coefficients are Boolean (bij = 0 or 1) [3] , [4], [5]. This matrix B represents a bipartite graph G. An interpretation of the canonical components of the bipartite graph will allow us to characterize the atomic stable parts of the set E endowed with Ɍ. We indeed show that in the irreducible sub graphs Gi of G (Gi (Si,Ti ;A(Gi)), the subsets Si of E (i=1,….,k) are the atomic stable parts for the partial order relation Ɍ An application is proposed for the temporal decomposition of the one-machine scheduling problem

Real time adaptive efficient cold start strategy for proton exchange membrane fuel cells

Cold start of proton exchange membrane fuel cells (PEMFCs) at sub-zero temperatures is perceived as one of the obstacles in their commercialization way in automotive application. This paper proposes a novel internal-based adaptive strategy for the cold start of PEMFC to control its operating current in real time in a way to maximize the generated heat flux and electrical power in a short time span. In this respect, firstly, an online parameter identification method is integrated into a semi-empirical model to cope with the PEMFC performances drifts during cold start. Subsequently, an optimization algorithm is launched to find the best operating points from the updated model. Finally, the determined operating point, which is the current corresponding to the maximum power, is applied to PEMFC to achieve a rapid cold start. It should be noted that the utilization of adaptive filters has escaped the attention of previous PEMFC cold start studies. The ultimate results of the proposed strategy are experimentally validated and compared to the most commonly used cold start strategies based on Potentiostatic and Galvanostatic modes. The experimental outcomes of the comparative study indicate the striking superior performance of the proposed strategy in terms of heating time and energy requirement. © 2018 Elsevier Lt

Efficient model selection for real-time adaptive cold start strategy of a fuel cell system on vehicular applications

The PEMFC maximum power is greatly influenced by subfreezing temperature and degradation phenomena. Therefore, a dependable model is required to estimate the power with respect to the variation of the operating conditions and state of health. Semi-empirical models are potent tools in this regard. Nonetheless, there is not much information about their cold environment reliability. This paper comprehensively compares the performance of some models (already tested in normal ambient temperature) in subfreezing condition to introduce the most reliable one for PEMFC cold start-up application. Firstly, seven models are compared regarding voltage losses and precision. Subsequently, the three most dependable ones are selected and experimentally compared at sub-zero temperature in terms of polarization curve estimation for three PEMFCs with different degradation levels. The results of this study indicate that the model introduced by Amphlett et al. has a superior performance compared to other ones regarding the characteristic's estimation in below-zero temperature

Effect of treated wastewater irrigation on physiological and agronomic properties of beans Vicia faba

The current study investigated the effect of two doses (50%, and 100 %) of treated wastewater (TWW)on biometric and physiologic parameters of Vicia faba beansafter 40 days of exposure. Our data showed a decrease in shoots and roots length and weight in plants amended with TWW. Moreover, a significant decrease in Chlorophyll ‘a\u27, ‘b\u27 and carotene content was observedin plants irrigated with 100% of TWW. These findings provided new insights on TWW reuse which can cause different types of stress as it may affect the development of cultivated crops

Comparative analysis of two online identification algorithms in a fuel cell system

Output power of a fuel cell (FC) stack can be controlled through operating parameters (current, temperature, etc.) and is impacted by ageing and degradation. However, designing a complete FC model which includes the whole physical phenomena is very difficult owing to its multivariate nature. Hence, online identification of a FC model, which serves as a basis for global energy management of a fuel cell vehicle (FCV), is considerably important. In this paper, two well-known recursive algorithms are compared for online estimation of a multi-input semi-empirical FC model parameters. In this respect, firstly, a semi-empirical FC model is selected to reach a satisfactory compromise between computational time and physical meaning. Subsequently, the algorithms are explained and implemented to identify the parameters of the model. Finally, experimental results achieved by the algorithms are discussed and their robustness is investigated. The ultimate results of this experimental study indicate that the employed algorithms are highly applicable in coping with the problem of FC output power alteration, due to the uncertainties caused by degradation and operation condition variations, and these results can be utilized for designing a global energy management strategy in a FCV. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

Structural, magnetic and vibrational characterization of the new organic-inorganic hybrid material, (C9H14N)2CoCl4

A new organic–inorganic hybrid material, bis (N, N-dimethylbenzylammonium) tetrachlorocobaltate (II), (C9H14N)2 CoCl4 was synthesized and analyzed by X-ray diffraction. Magnetization was used to investigate the magnetic properties. The structure was determined at room temperature in the triclinic space group P-1 with the following parameters: a = 10.491 (5)Å, b = 14.207 (2)Å, c = 16.187 (3)Å, α = 87.76 (3)°, β = 88.436 (8)°, γ = 89.897 (10)° and Z = 2. The structure can be described by the alternation of organic-inorganic layers parallel to (110) plan. The different components are connected by the Nsingle bondH⋯Cl hydrogen bonds between the cation and the anionic group [CoCl4]2-. Raman and infrared spectra were used to gain more information of the title compound. An assignment of the observed vibration modes is reported. This compound exhibits an antiferromagnetic (AFM) to paramagnetic (PM) phase transition at a temperature (TN) lower than 2 K. The values of paramagnetic Curie–Weiss temperature θCW, the nearest neighbor interaction Jnn, the classical nearest neighbor J cl and the dipolar Dnn interactions’ emphasize the existence of an antiferromagnetic interaction between the neighboring cobalt ions.publishe

An online self cold startup methodology for PEM fuel cells in vehicular applications

This paper puts forward an adaptive cold start strategy for a proton exchange membrane fuel cell (PEMFC) based on maximum power mode. The proposed strategy consists of a water evacuation process after PEMFC shutdown and a self-heating process at PEMFC cold startup. To maximize the performance of the suggested strategy, an optimal operating condition for the cold start procedure is sought first. In this respect, an experimental parametric study is performed to explore the impact of fan velocity, micro-short circuit, anode pressure, and purge procedure on the PEMFC cold start performance. After laying down the proper conditions, the proposed cold start procedure is implemented on a test bench for experimental validations. The self-heating process is based on an online adaptive algorithm that maximizes the PEMFC's internal heat depending on its operating parameters' variation. In fact, this algorithm attempts to keep the current density at high levels, leading to PEMFC's performance improvement achieved by membrane hydration and temperature increase. The experimental results confirm the effectiveness of the proposed strategy, which presents a fast and cost-effective PEMFC's cold start. © 2020 IEEE

Investigating the impact of energy source level on the self-guided vehicle system performances, in the Industry 4.0 context

Automated industrial vehicles are taking an imposing place by transforming the industrial operations, and contributing to an efficient in-house transportation of goods. They are expected to bring a variety of benefits towards the Industry 4.0 transition. However, Self-Guided Vehicles (SGVs) are battery-powered, unmanned autonomous vehicles. While the operating durability depends on self-path design, planning energy-efficient paths become crucial. Thus, this paper has no concrete contribution but highlights the lack of energy consideration of SGV-system design in literature by presenting a review of energy-constrained global path planning. Then, an experimental investigation explores the long-term effect of battery level on navigation performance of a single vehicle. This experiment was conducted for several hours, a deviation between the global trajectory and the ground-true path executed by the SGV was observed as the battery depleted. The results show that the mean square error (MSE) increases significantly as the battery’s state-of-charge decreases below a certain value