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Eight (8) pyrazine derivatives were tested as mild steel corrosion inhibitors in a simulated oil field acidizing environment. Immersion tests and DFT calculations were adopted for the study. Immersion tests were carried out at 0.2 wt. % inhibitor concentration at 25 o C for a total duration of 24 h. The results showed that all the pyrazine derivatives tested protected the steel to various extents in the acid medium. Pyrazine carboxamide (Pyrazine E) exhibited the highest inhibition efficiency among the pyrazine derivatives investigated. The resulting molecular descriptors obtained from DFT calculations were correlated with the experimental inhibition efficiency to develop QSAR model. Multiple linear regression was utilized to correlate the inhibition efficiency with the molecular descriptors at a 95% confidence interval. This work revealed that the inhibition efficiencies of the studied pyrazine molecules were influenced by their ELUMO, dipole moment (DM) and the molecular volume (MV). Based on the QSAR model developed, four new pyrazine derivatives were designed, and their inhibition efficiencies predicted.

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Macroreticular resins based on cross-linked polystyrene, XAD-2 and XAD-4, have been "coated" with tri-n-dodecylmethylammonium iodide (TLMAI) and n-hexadecyltrimethylammonium bromide (CTAB) along with a polystyrene (PS) sample. The anions on the quaternary ammonium salts have been labeled with the anion label l-oxyl-2,2,5,5-tetramcthylpyrrolidine-3-carboxylate (label 517). EPR spectra were taken as a function of temperature. The nitrogen hyperfine coupling constants along thedirection. A, were determined from the rigid limit spectra at 77 K. Rotational correlation times used in Arrhenius plots were calculated from the empirical formula rR = (1 -S)b for the Brownian model (a and h are constants and S = A.'/A., the ratio of the outer EPR hyperfine extrema separation at a given correlation time to the rigid limit separation). From a consideration of other published systems, the Az values can be attributed to variations in polarity and hydrogen bonding within the host matrix. The Arrhenius plots were not linear; they consisted of two linear regions for XAD-4(CTAB) and XAD-4(TLMA1) and three linear regions for XAD-2(CTAB), XAD-2(TLMA1), and PS(TLMAI). The low-temperature regions correspond to energy barriers to rotational motion about covalent bonds while the middle range (roughly 120-250 K) corresponds to breaking of hydrogen bonds such as would be involved in the presence of water. Information on anisotropy of rotational diffusion obtained from the high-temperature spectra in the temperature region 370-480 K revealed that motion was highly anisotropic for the XAD-4(CTAB) and moderately so for XAD-2(TLMAI) and XAD-2(CTAB), while it was isotropic for PS(TLMAl). A model is developed to explain qualitatively the anion behavior, taking into consideration the resin pore structures, interaction of the hydrocarbon chains of the cations with the resin framework, the degree of ordering of the charged heads, and their interaction with and retention of water.

The objective of the Economic Dispatch problem is to allocate the total power generation to the generating units to meet a given total active-load demand. This problem cannot be solved using the traditional analytical approaches, due to the high non-convexity of the objective cost functions. This work introduces an iterated local search algorithm that employs the great deluge search method to solve the non-convex Economic Dispatch problem. This algorithm has demonstrated efficiency in solving various NP-hard problems but has never been applied to solve the Economic Dispatch problems. The performance of the proposed algorithm is compared to those of other search algorithms using standard benchmarks. The simulation results verified the superior performance of the proposed algorithm compared to the other approaches. Index Terms-Economic dispatch, valve-point loading effects, non-convex optimization, great deluge algorithm. I. INTRODUCTION The Economic Dispatch (ED) problem ensures economical-based allocation of the total active load demand to a number of committed power generation units, while satisfying the generation units' operational, physical, and economic constraints [1], [2]. The ED problem is a non-convex optimization problem and it is considered NP-hard problem. This due to the high nonlinearity of the power system networks and the associated nonlinear operational and management constraints like power generation capacities, transmission power losses, multiple-fuel options, valve-point loading effects, and generation ramp-limits. Therefore, many heuristics have been used to solve the non-convex ED problem and it has been shown that, they can generate competitive results in terms of quality and effectiveness of the obtained solutions (e.g. [3]-[8]). Other approaches have been proposed to solve the ED problem like the adaptive reinforcement learning approach [9], where the eligibility traces were used to speed up the convergence process.