Introduction
Imagining a world without electricity is very difficult today if not impossible owing to the miraculous and wonderful things that energy from electricity has contributed in improving the conditions of living, creation of wealth as well as providing extensive community linkages. This is as well the reason for expected continuous demand in electricity today and in the future. Production of the ever increasing volumes of electricity required for used in everyday life poses to be a great challenge that requires attention. the world is facing a future projected in the increase in demand for electricity which needed to be fulfilled and with the availability of scientific knowledge as well as better capabilities of technology with which the world is equipped, this demand can be serviced to greater levels if not fully met [1].
Problem Formulation
There are power generators serving our operating scattered facilities which are the backbone since they are the primary power supply to the gas processing facilities and associated wellheads. Facilities are scattered around within a distance of 200 km the farthest and 70 Km the nearest skid road travelling. There are 5 running facilities: A, B1, B2, B3, and B4 with a total number of 10 main generators and 5 Emergency Diesel generators.
Facility A: 1 Gas generator (Prime), 1 Dual Fuel generator (Standby), and 1 emergency diesel generator
Facilities B1, B2, B3, and B4: each facility has 2 gas generators (1 prime and 1 standby) plus 1 emergency diesel generator at each.
There is need to study this power system that is adopted by the facilities with regards to the performance as well as the needs of output besides the high end cost of generation of power. The upfront cost in this case is inclusive of the cost of installation, annual energy costs which represent the yearly expenses as well as the net costs of energy that demonstrate the total cost of running the energy system inclusive of the maintenance costs.
Methodology
A stistical approach will be used in the optimization of the output which would define the power system of the mileage. The Define-Measure-Analyze-Improve-Control, DMAIC, is the ideal Six Sigma methodology of problem solving that would be used in a problem where there is an existing process or even service or product as is the case presented in this study. Variation is treated as the energy and variations from the specifications of the customer in the product or even the process turns out to be the main challenge [2]. DMAIC is used in the identification of the main requirements, standard tools, deliverables as well as tasks used in the tackling of the identified problem.
Measure Phase
This phase involves deeply studying the overall undertaken challenge which finally results in the outcomes thereby coming up with numerous parameters whose impacts have significant effects on the overall performance of the power system. Historical data has been gathered from the logbook of the power system for a period of one year and the collected data is a reflection of the performance of the power system in which all the information regarding consumption of electricity is contained [3].
Figure 1: Monthly consumption of diesel
Analyse Phase
The likely factors attributed to the poor mileage of the generator set and the power system at large has been illustrated using an Ishakawa diagram that has frozen these entire factors. The top ribs of the diagram structured using parameters that are linked to the part of the generator if the power systems with the bottom ribs are designated for the different variables of the engine parts [4]. The identified factors for the poor performance of the generator are grouped in three upon analysis including Noise variables, controlled variables as well as critic variables
Figure 2: Mileage Fish Bone Diagram
Optimization of the generator set mileage has been done using the technique of design of experiment during the improve phase. Three major factors have been identified from the analyze phase with each factor being defined with regard to the high as well as low levels. The factors include coolant temperature, load, and pressure of the lube oil. The number of trials used is 33=27 with the experiment being replicated to ensure attainment of results that are precise and reliable [5]. A cumulative 18 experiments were conducted of the factorial designed and the corresponding dependent variable or even output which is the mileage was put as required in the matrix
Table 1: Outcomes of designed experiment
The Minitab Optimizer tool is used in predicting the optimize figures for the chosen input factors for improving the mileage of the power systems which involve enhancements of the mileage of each of the generator sets. Factors input having low as well as high values have been fed to optimizer with the aim value being about 5.5 units per liter as the desirable value of mileage. The Minitab predicted the values given in red at a confidence level of 95% to be the optimized values for the corresponding factors of input and hence the solution of the design of experiments model used [7].
Figure 4: Minitab Optimizer forecasted mileage values
Control/Planning Phase
This is the last phase of the DMAIC technique analysis and involves the planning conducted in such a way that the optimized parameters are optimized to ensure the best output is attained [10]. Parameter including pressure of the lube oil, load, seasonal impact as well as coolant temperature need to be considered to maintain at the enhanced values to attain maximum attainable mileage
Figure 5: Attained Outcomes
The behavior of the mileage prior to and after improve phase has been collected and the critical to process dimensions have been simulated at the proposed optimized levels and the attained mileage was determined in reliability,
Plan or Recommendations
Seasonal impact: the generator system might have been placed in an open shade. It is proposed that a complete generator room is constructed taking into consideration the needs for ventilation to house all the generators used in the facilities [8]
Diesel generator set process trial to quality: These given process parameters should be kept at provided values for the optimal performance of generator sets mileage
Temperature of the coolant=
Load should be 10 kW
Pressure of lube oil should be
General engine & alternator control: The failure mode and effect analysis should be conducted on the generator sets and the remedies established as a result of the simulation to be adopted for better performance [9].
Conclusion
This research study deduces that the standard deviation of the process of power generation has been reduced besides a shift in the in the power process generation mean by the studied generator sets. In as much as Six Sigma was initially developed for use in manufacturing process, it is usable in every sector day, more than three decades since its development. In this study, a step is taken in increase the performance of a set of generator used in a facility through optimization of the different parameters of operation hence resulting in reduced costs as well as lowering the overall loses in finances. This study hence is a presentation of a novel case to the application of the methodology of Six Sigma within the power generation and supply sector. The efficient of Six Sigma has been kept through successfully implementing the DMAIC approach stepwise in power sectors that are often given limited attention if not completely ignored. The findings of this study may thus be used as references, bringing operational excellent via optimization in other power supply systems in sustaining various business processes with enhanced consistency globally.
References
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[3] Eng, G.P.W.P., 2017. SIX SIGMA AS THE METHOD OF PROCESSES’EFFICIENCY IMPROVEMENT IN ENERGY INDUSTRY. International Multidisciplinary Scientific GeoConference: SGEM: Surveying Geology & mining Ecology Management, 17, pp.773-780
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