This study develops a model of a closed-loop supply chain (CLSC) network which starts with the suppliers and recycles with the decomposition centers. As a traditional network design, we consider minimizing the all transportation costs and the raw material purchasing costs. To pay attention for the green impacts, different transportation choices are presented between echelons according to their CO 2 emissions. The plants can purchase different raw materials in respect of their recyclable ratios. The focuses of this paper are conducting the minimizing total CO 2 emissions. Also we try to encourage the customers to use recyclable materials as an environmental performance viewpoint besides minimizing total costs. A multi objective linear programming model is developed via presenting a numerical example. We close the paper with recommendations for future researches.
The recent decades have seen the increase in demand for reliable and clean form of electricity derived from renewable energy sources. One such example is solar power. The challenge remains to maximize the capture of the rays from the sun for conversion into electricity. This paper presents fabrication and installation of a solar panel mount with a dual-axis solar tracking controller. This is done so that rays from the sun fall perpendicularly unto the solar panels to maximize the capture of the rays by pointing the solar panels towards the sun and following its path across the sky. Thus electricity and efficiency increased.
In recent years, injection of renewable energy such as solar power into the power grid is increasing. However, inclusion of large-scale intermittent-type renewable energy requires better management in proper understanding of grid’s variable characteristics and its protection systems. In this paper, the investigation on overvoltage issue is illustrated. Overvoltage in distribution feeder occurs when large amount of solar power is injected at low power demand. Another investigation is on false operation of overcurrent relays due to reverse power to the 33 kV loads. The potential solutions to the two issues are illustrated in the small-sized power grid system using bi-directional inverters on AC buses in charging battery banks and adjusting the relay current settings. The benefits of solar power injection are illustrated whereby output power from generators is decreased and transmission losses are reduced. Electrical Transient Analysis Program (ETAP) was used for investigations.
Artificial neud networks (ANN) have become very popular in many control applications due to their high computation rate and ability to handle nonlinear hctions. This paper presents a new scheme for the speed control of a separately excited dc motor. The signals corresponding to the motor speed error and change in speed error are used as the inputs. The artificial neural network outputs the appropriate control signals for achieving the desired speed response even under wide variations in motor parameters. The training patterns are generated using fuzzy logic principles, and the effectiveness of the proposed scheme is illustrated using simulation StUdiCS. L INTRODUCTIONThe dc motor drives have occupied a wide spectrum of applications for variable speed drive. Separately excited dc motors find many applications in industries where precise speed control over wide range is required. Normally closed loop operation with PI controllers in the inner current loop and outer speed loop is employed for speed control [l]. These controllers are designed and implemented using the values of motor parameters, these are, armature resistance &, armature inductance La, viscous friction coefficient B and moment of inertia J supplied by the mufacturers or the values of parameters obtained through measurements. With these controllers the response of the system may become unsatisfactory due to changes in parameters caused by aging or errors in measurements. It is therefore important to consider the parameter variations into account while designing and implementing the controllers. Adaptive controllers are used to cope up with the parameter variations. They are not advisable when fast response is required as it takes much computation time in calculating the control input Also the hardware implementation is very complex [2]. In recent years the fuzzy logic has gained much popularity in many control applications. Fuzzy logic is an attractive technique when the plant model is complex or ill defined. The main disadvantage with fuzzy control system is the heavy computation burden in translating the 1ingUistic control rules into the corresponding control actions. A trained neural network is promising as it requires very less computation time and memory. It is therefore reasonable to combine the merit of fuzzy systems and neural network, namely the pattern recognition ability of neural network and adaptive ability of fuzy systems. The application of neural nebork based on the fuzzy set theory is presented in this paper. II. FUZZY CONTROL ALGORITHM FOR DC MOTOR DRIVEIn order to achieve satisfactory response, i.e. fast tracking of set point changes, small rise time, negligible maximum speed dip due to load changes and zero steady-state error, the speed feedback loop is used [3]. Exact dynamic drive system model is not required as the fuzzy controller is used for feedback loop. The performance of the fuzzy controller is insensitive to parameter variations. In brief, the development of the fuzzy controller for the dc motor control is described. DYNA...
In this paper, the modified S-curve membership function methodology is used in a real life industrial problem of mix product selection. This problem occurs in the production planning management where by a decision maker plays important role in making decision in an uncertain environment. As analysts, we try to find a good enough solution for the decision maker to make a final decision. An industrial application of fuzzy linear programming (FLP) through the S-curve membership function has been investigated using a set of real life data collected from a Chocolate Manufacturing Company.
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