Modified rice husk and activated carbon filters for the removal of organics and heavy metals in water

Discharge of untreated industrial effluents containing heavy metals and organics is hazardous to the environment because of their toxicity and persistent nature. At the same time, agricultural waste poses disposal challenges, which can be converted into value added products like adsorbents that could serve as tools for contaminants abatement. Previous findings proved that, adsorption was a sustainable, economical and lucrative separation technique for the removal of such contaminants. This thesis presents the fabrication of a filter for the removal of organics and heavy metals in water which was prepared from treated rice husk and modified activated carbon (AC). The analysis of AC via Brunauer-Emmett-Teller (BET) surface area and scanning electron microscopy evidenced porosity of 707 m2/g as surface and a pore volume of 0.31 cm3/g. The elemental and thermogravimetric analysis proved that AC contain 48. 7% carbon, while the Fourier transform infrared spectroscopy shows that the surface contains functional groups such as O-H, C=C, C-O, C-O-C and C-H. The experimental results were fitted with fixed-bed adsorption models to understand the adsorbate-adsorbent relationship. Fixed-bed adsorption studies show that, the highest adsorption capacity of 248.2 mg/g and 234.12 mg/g for BPA and phenol respectively was obtained at 250 mg/L concentration and 9 mL/min flow rate. The results also revealed 73 % and 87 % as the highest removal capacity for heavy metal Pb and Cd respectively at 20 mg/L concentration and 9 mL/min flow rate. For sustainability, regeneration of the spent AC was carried out in a microwave which showed 75% yield after five cycles, while the rice husk was eluted with 0.lM hydrogen chloride and 37.8% efficiency was achieved after three successive cycles. The UV lamp incorporated in the filter shows total inactivation of E. coli after 7 minutes

Multi-objective tabu search for the location and sizing of multiple types of FACTS and DG in electrical networks

Flexible AC transmission systems and distributed generation units in power systems provide several benefits such as voltage stability, power loss minimization, thermal limits enhancement, or enables power system management close to the limit operation points; and by extension, economic benefits such as power fuel cost and power loss cost minimization. This work presents a multi-objective optimization algorithm to determine the location and size of hybrid solutions based on a combination of Flexible AC transmission systems devices and distributed generation. Further, the work expands the types of FACTS usually considered. The problem is solved by means of a Tabu search algorithm with good results when tested in a network of 300 nodes

Improvement of active distribution systems with high penetration capacities of shunt reactive compensators and distributed generators using Bald Eagle Search

This work proposes an intelligent allocation of distributed generation (DG) units and shunt reactive compensators (SRC) with high penetration capacities into distribution systems for power loss mitigation using the Bald Eagle Search (BES) optimization algorithm. The intelligent allocation causes a reduction in voltage variations and enhances the voltage stability of the systems. The SRC units include shunt capacitors (SC), Static Var Compensators (SVC), and Distribution Static Compensators (DSTATCOM), which are determined according to their capacities. The optimization study includes the 33-bus and the 118-bus distribution systems as medium to large systems. Performance parameters, including the reactive power loss, Total Voltage Deviation (TVD), and Stability Index (SI), besides the power loss, are recorded for each optimization case study. When the BES algorithm optimizes 1, 2, and 3 DG units operating at optimal power factor (OPF) into the 33-bus systems, percentage reductions of power loss reach 67.84%, 86.49%, and 94.44%, respectively. Reductions of 28.26%, 34.47%, 35.24%, and 35.44% are achieved in power loss while optimizing 1, 3, 5, and 7 SRC units. With a combination of DG/SRC units, the power loss reductions achieve 72.30%, 93.89%, and 97.49%, optimizing 1, 3, and 5 pairs of them. Similar reductions are achieved for the rest of the performance parameters. With high penetration of compensators into the 118-bus system, the percentage reductions of power loss are 29.14%, 73.27%, 83.72%, 90.14%, and 93.41% for optimal allocations of 1, 3, 5, 7, and 9 DG units operating at OPF. The reduction reaches 11.15%, 39.08% with 1 and 21 devices when optimizing the SRC. When DG SRC units are optimized together, power loss turns out to be 32.83%, 73.31%, 83.32%, 88.52%, and 91.29% with 1, 3, 5, 7, and 9 pairs of them. The approach leads to an enhanced voltage profile near an acceptable range of bus voltages, reduces the voltage fluctuation substantially, and enhances the system stability. The study also ensures the BES algorithm’s capability to solve these nonlinear optimization problems with high decision-variable numbers

Hyper-heuristic strategies for optimal power flow problem with FACTS devices allocation in wind power integrated system

This research provides hyper-heuristic methodologies for solving Optimal Power Flow (OPF) issues in power system networks with Flexible AC Transmission Systems (FACTS) devices. OPF can be treated as one of the demanding challenges in the power system operating networks. To address the problems of loss and cost reduction, three types of FACTS devices will be studied in this paper: Static VAR Compensator (SVC), Thyristor-Controlled Series Compensator (TCSC), and Thyristor-Controlled Phase Shifter (TCPS). Two high level hyper-heuristic (HHH) approaches, called Exponential Monte Carlo with counter (EMCQ) and randomly select-Only Improving (OI), are employed as high-level metaheuristic to select and leverage the effectiveness of four low-level metaheuristics (LLH). These low-level metaheuristics comprise the Moth-Flame Optimizer (MFO), Barnacles Mating Optimizer (BMO), Teaching-Learning Based Optimization (TLBO) and Gradient-Based Optimizer (GBO). The usage of HHH solving the OPF problem is tested on the modified IEEE 30 bus system that integrates the thermal generators with the wind power. Findings of the study demonstrated the promising results by HHH which manages to outperform all the selected LLH algorithms

Optimal placement of statcom controllers with metaheuristic algorithms for network power loss reduction and voltage profile deviation minimization.

Masters Degree. University of KwaZulu-Natal, Durban.Transmission system is a series of interconnected lines that enable the bulk movement of electrical power from a generating station to an electrical substation. This system suffers from unavoidable power losses and consequently voltage profile deviation which affects the overall efficiency of the system; hence the need to reduce these losses and voltage magnitude deviations. The existing methods of incorporation of static synchronous compensator (STATCOM) controllers to solve these problems suffer from incorrect location and sizing, which could bring about insignificant reduction in transmission network losses and voltage magnitude deviations. Hence, this research aims to reduce transmission network losses and voltage magnitude deviation in transmission network by suitable allocation of STATCOM controller using firefly algorithm (FA) and particle swarm optimization (PSO). A mathematical steady-state STATCOM power injection model was formulated from one voltage source representation to generate new set of equations, which was incorporated into the Newton-Raphson (NR) load flow solution algorithm and then optimized using PSO and FA. The approach was applied to IEEE 14-bus network and simulations were performed using MATLAB program. The results showed that the best STATCOM controller locations in the system after optimization were at bus 11 and 9 with the injection of shunt reactive power of 8.96 MVAr, and 9.54 MVAr with PSO and FA, respectively. The total active power loss for the network under consideration at steady state, with STATCOM only and STATCOM controller optimized using PSO and FA, were 6.251 MW, 6.075 MW, 5.819 MW and 5.581 MW, respectively. The corresponding reactive power were 14.256 MVAr, 13.857 MVAr, 12.954 MVAr and 12.156 MVAr, respectively. In addition, bus voltage profile improvement indicates the effectiveness of metaheuristic methods of STATCOM optimization. However, FA gave a better power loss and voltage magnitude deviations minimizations over PSO. The study concluded that FA is more effective as an optimization technique for suitably locating and sizing of STATCOM controller on a power transmission system.Publications listed on page iii

Optimization of FACTS devices : classification, recent trends, and future outlook

Since the inception of industrialization, power system has been an indispensable aspect of economy. With the progression of time, technology has impalpably commingled into our lifestyle. Alongside blooming technologies, energy demand is proliferating and power companies are begetting energy at their best to quench it. Growing reliance on power system has brought its quality into more advertence. Various electronic devices and topologies have been invented to enhance power quality and reliability; numerous others are still underway. During the course, power system has grown to an intricate network of sources, loads and control devices, leading to various issues such as transmission congestion and high losses. This paper discusses ways to ameliorate congestion and gives an overview of relationship between our present energy resources and ecological threats like global warming. Moreover, it points out various power system problems such as energy losses and transients. The necessity of FACTS devices has also been elaborated alongside their classification and comparison. Finally, numerous topologies and optimization methods proposed in the technical literature have been classified and analyzed to alleviate power system conundrums, and a glimpse into future energy trends is presented

Intelligent power system operation in an uncertain environment

This dissertation presents some challenging problems in power system operations. The efficacy of a heuristic method, namely, modified discrete particle swarm optimization (MDPSO) algorithm is illustrated and compared with other methods by solving the reliability based generator maintenance scheduling (GMS) optimization problem of a practical hydrothermal power system. The concept of multiple swarms is incorporated into the MDPSO algorithm to form a robust multiple swarms-modified particle swarm optimization (MS-MDPSO) algorithm and applied to solving the GMS problem on two power systems. Heuristic methods are proposed to circumvent the problems of imposed non-smooth assumptions common with the classical approaches in solving the challenging dynamic economic dispatch problem. The multi-objective combined economic and emission dispatch (MO-CEED) optimization problem for a wind-hydrothermal power system is formulated and solved in this dissertation. This MO-CEED problem formulation becomes a challenging problem because of the presence of uncertainty in wind power. A family of distributed optimal Pareto fronts for the MO-CEED problem has been generated for different scenarios of capacity credit of wind power. A real-time (RT) network stability index is formulated for determining a power system\u27s ability to continue to provide service (electric energy) in a RT manner in case of an unforeseen catastrophic contingency. Cascading stages of fuzzy inference system is applied to combine non real-time (NRT) and RT power system assessments. NRT analysis involves eigenvalue and transient energy analysis. RT analysis involves angle, voltage and frequency stability indices. RT Network status index is implemented in real-time on a practical power system --Abstract, page iv