This paper covers the Ant Colony OPTIMISATION Algorithm (ACO) as an OPTIMISATION method and discusses and recommends the utilization of the model in design and analysing of varies parameters related in the oil and gas pipeline network systems. This is to achieve the optimum length of the pipeline, pressure and flow rate. Ant Colony OPTIMISATION Algorithm (ACO) is capable of finding minimum path between several paths with their limitations and decreases pipe lengths from the sources to their destinations. It can be used in petroleum and gas refineries, transmissions and distributions lines. The theoretical and mathematical example of Ant Colony OPTIMISATION Algorithm (ACO) between two places were carried and calculated. Optimum length is calculated about100 cm with optimum pressure about 2440 psi and flow rate about 83*10 6 3 𝑚 𝑚 /ℎ . An example was designed based on the random variables results from 10 to 96 km for MATLAB Software and 0. 1 to 1 km for ANTCOL Software as Stochastic Variables (SV) of length (Km) between 14 stages which may show as „ SV-Matrix = Randi ([10 96], 14)‟ and Randi ([0. 1 1. 5], 14)” . For each iterations the SV matrixes are showed varies range of integrity. An example was assumed between 14 places with varies range of limitations which will be occurred during a pipeline project (FIG. 6 as an initially supposition graph) to find minimum path between stages to conclude optimum range of pressure and flow rate of oil and gases based on the optimum minimum paths of pipeline network systems. SV matrixes are used based on the MATLAB Code and ANTCOL software by the CPU core 2 Duo “ Intel” based on the ACO algorithm formulas. The output lines, graphs and diagrams of ACO algorithm are showed the minimum optimum path between 14 stages about 526 km with start point from station 6 and optimum flow-rate 0. 09810 6 𝑚 𝑚 3 /ℎ 𝑟 𝑟 and pressure drop about 714. 638 bar while 3. 631 km as minimum length with optimum flow rate 1. 515480*10 6 𝑚 𝑚 3 /ℎ 𝑟 𝑟 and pressure drop about 1192. 83 bar are found by ANTCAL results. The results proved the ability of ACO algorithm to find the optimum path with its effects on the other importance parameters, especially in the pipeline network systems, distribution and transmission lines and refineries.

An efficient formulation for the robust shape optimization of aerodynamic objects is ‎introduced in this paper. The formulation has three essential features. First, an Euler ‎solver based on a second-order Godunov scheme is used for the flow calculations. ‎Second, a genetic algorithm with binary number encoding is implemented for the ‎optimization procedure. The third ingredient of the procedure is the use of previous ‎flow that is closest in terms of geometric parameters to the new shape as an initial ‎condition for the new function evaluation. This makes the solution towards the final ‎value progressively faster and reduces the computer time for the convergence of the ‎algorithm. The algorithm is used to optimize two different problems, a simple bump ‎problem, and a two-dimensional transonic airfoil problem using an Euler solver solver ‎equation. The results indicate that GA/flow algorithm is robust and can optimize a ‎wide range of problems with a minimum implementation effort.

So far a large number of algorithms have been developed for OPTIMISATION of ultimate pit limits, most of which follow deterministic rules. In this paper a new algorithm is introduced, which follows a probabilistic logic using Markov chains. The algorithm is implemented on a transition matrix that correspond the 2D conventional economic block model of the orebody. Probability of mining a block is proportional to the profit it may produce. Applying this algorithm, probability of mining each block is obtained and finally the optimum pit is defined as the pit, which provides with the highest probability of mining. A 2D analysis of the problem is discussed in this paper; however, it is not difficult to expand the problem to 3D cases.

Background: An effec ve polymer gel dosimeter can be fabricated by varying the composi on of its chemical components. Materials and Methods: The MAGAT gel dosimeter formula ons that used different composi ons of Methacrylic acid (MAA) and gela n were extensively inves gated in the present study according to the R2– dose response and R2– dose sensi vity. The irradia on of MAGAT gel was performed by 6-MV photon beam at a dose range 1 to 10 Gy and was imaged by 1. 5T Magne c Resonance Imaging (MRI). The dose response of MAGAT gel dosimeter was obtained from spin-spin relaxa on rate (R2) of MRI signal. Results: The MAGAT gel dosimeter composed of 5% gela n and 6% MAA gave the highest sensi vity (1. 1180 s-1Gy-1). Conclusion: Understanding the effects of the composi onal changes will help to clarify the mechanisms involved in the dose response of the MAGAT gel dosimeter.

Few algorithms are available for economic optimization of underground mining area. This is mainly because there are a variety of underground mining methods with various restrictions and conditions and the underground mining parameters are more complex. Therefore, it does not allow the development of general optimization tools, especially, software tools or computer programs. Based on the Global OPTIMISATION for Underground Mining Area (GOUMA) algorithm, a computer program , GOUMA-CP, was developed to implement the mentioned algorithm. GOUMA enjoys a new system of modelling named "Variable Value Economic Model" (VVEM) and benefits from the mathematical proof and provide the true optimal solution according to the conditions and environment it is applied. This paper includes a description of the GOUMA-CP program in details and the corresponding algorithm briefly. This computer program has successfully been applied to optimise underground mining area of a gold mine in Australia.

A large number of algorithms have been developed for optimization of mining limits in open pit boundaries, however, little amount of research is available for underground cases. This is mostly due to variability of the underground mining methods, complexity of economic factors in underground mines and lack of acceptability of computer algorithms among the underground mine design practitioners. This paper presents a new algorithm to optimize ultimate stope geometry, using the dynamic programming technique. The algorithm employs a conventional 20 economic block model, called the "primary model". The minimum stope height and length are imposed to the primary to construct intermediate and final models. The dynamic programming algorithm is then applied on the final model to maximize the stope total net value. The task is performed through a recursive formula, which consists of two criteria. The proposed dynamic programming algorithm is best suited for vein type orebodies using any type of underground mining methods.