Power optimization for a hydrocarbon industrial plant using a genetic algorithm

In this paper, a genetic algorithm (GA) is considered for optimizing electrical power loss for a real hydrocarbon industrial plant as a single objective problem. The subject plant electrical system consists of 275 buses, two gas turbine generators, two steam turbine generators, large synchronous motors, and other rotational and static loads. The minimization of power losses (J1) objective is used to guide the optimization process, and, consequently, the injected power into the grid (PRInject) is increased. The results obtained demonstrate the potential and effectiveness of the proposed approach to optimize the power consumption. Also, in this paper a cost appraisal for the potential daily, monthly and annual cost saving will be addressed

Evolutionary Computation, Optimization and Learning Algorithms for Data Science

A large number of engineering, science and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measurement technologies, e.g., cameras, smart phones, health sensors, smart electricity meters, and environment sensors. Hence, it is imperative to develop efficient algorithms for generation, analysis, classification, and illustration of data. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. We focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data. This motivates researchers to think about optimization and to apply nature-inspired algorithms, such as evolutionary algorithms (EAs) to solve optimization problems. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. Researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms

An Energy management system structure for Neighborhood Networks

The accelerated integration of Renewable Energy Resources (RES) and Dispersed Generations (DGs) has contributed to big shifts in the power grid. The incorporation of home-scale electricity generators (HSEGs) into the Neighborhood Networks is considered and has contributed to the development of more stable and efficient smart grids. The implementation of this system includes an integrated control system along with a power electronic converter. In this paper, a power electronic-based HEMS scheme is presented for the neighborhood network including adjacent HSEGs to achieve an energy positive/neutral neighborhood. A multi home energy hub neighborhood network (MHEHNN) is classified in terms of system structure, functionalities, and energy management system. Different scenarios are investigated to evaluate the significance of proposed control strategy for the case study (a system with two HEHs and two conventional buildings (CBs)) using MATLAB/SIMULINK simulations. A large-scale MHEHNN is often simulated in order to test system performance on a broader scale. The findings obtained reveal that the HEHs offer more resources to the MHEHNNN under the suggested scheme (about 98% more than conventional scheme). As a result, by the sale of surplus power, they gain more. By avoiding import payments from the grid, the energy cost of CBs is minimised. Around 98% of the entire day is also decreased by the grid load

Effect of plant growth regulators on regeneration of the endangered medicinal plant Calligonum comosum L. Henry in the Kingdom of Bahrain

The use of in vitro techniques for conserving plant biodiversity and protecting rare and endangered multipurpose plant species is considered as one of the most important ex-situ conservation policies. Development of an efficient in vitro regeneration protocol of Calligonum comosum is important and that has achieved to protect the endangered multipurpose medicinally important desert plant in the Kingdom of Bahrain. Nodal segments were used as explants source and the effect of various plant growth regulators (PGRs) were studied for responses and to regenerate the whole plants in modified Murashige and Skoog (MS) media through direct and indirect organogenesis via callus induction. 50% explants of C. comosum responded to initiate shoot in presence of 4.44 µM BAP with 2.68 µM NAA after four weeks of culture while 40% shoot initiation response was the highest value in presence of 9.29 µM KI with 5.37 µM NAA after 4 weeks of culture among the treatments of KI with NAA. The highest callus induction rate of 100% was found in media containing 9.29 µM KI and 5.37 µM NAA after four weeks. Multiple initial shoots those originated from nodal segments develop calli and showed organogenic differentiation of shoots in presence of BAP and IAA. The highest shoot multiplication frequency of 15 was observed while the shoots initiated in media contained 4.44 µM BAP with 2.85 µM IAA and were transferred to 8.56 µM IAA with 2.22 µM BAP. Shoot multiplication and shoot regeneration capacity was compared in different media and the highest performance of 234 shoots /explants after second multiplication was observed while shoots initiated in presence of 13.3 µM BAP and 5.71 µM IAA. As a precautionary approach to conserve the endangered medicinal plant species in the Kingdom of Bahrain, the application of in vitro culture is considered as an important alternative method in ex situ conservation strategy in the present study.Keywords: Calligonum comosum, endangered plants, ex situ conservation, in situ conservation, organogenesis, plant regeneration, tissue culture.African Journal of Biotechnology, Vol 13(25) 2513-252