Integration of New Technologies into Existing Mature Process to Improve Efficiency and Reduce Energy Consumption
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Optimal operation of plants is becoming more important due to increasing competition and small and changing profit margins for many products. One major reason has been the realization by industry that potentially large savings can be achieved by improving processes. Growth rates and profitability are much lower now, and international competition has increased greatly. The industry is faced with a need to manufacture quality products, while minimizing production costs and complying with a variety of safety and environmental regulations. As industry is confronted with the challenge of moving toward a clearer and more sustainable path of production, new technologies are needed to achieve industrial requirements. In this research, a new methodology is proposed to integrate so-called new technologies into existing processes. Research shows that the new technologies must be carefully selected and adopted to match the complex requirements of an existing process. The new proposed methodology is based on four major steps. If the improvement in the process is not sufficient to meet business needs, new technologies can be considered. Application of a new technology is always perceived as a potential threat; therefore, financial risk assessment and reliability risk analysis help alleviate risk of investment. An industrial case study from the literature was selected to implement and validate the new methodology. The case study is a planning problem to plan the layout or design of a fleet of generating stations owned and operated by the electric utility company, Ontario Power Generation (OPG). The impact of new technology integration on the performance of a power grid consisting of a variety of power generation plants was evaluated. The reduction in carbon emissions is projected to be accomplished through a combination of fuel switching, fuel balancing and switching to new technologies: carbon capture and sequestration. The fuel-balancing technique is used to decrease carbon emissions by adjusting the operation of the fleet of existing electricity-generating stations; the technique of fuel-switching involves switching from carbon-intensive fuels to less carbon-intensive fuels, for instance, switching from coal to natural gas; carbon capture and sequestration are applied to meet carbon emission reduction requirements. Existing power plants with existing technologies consist of fossil fuel stations, nuclear stations, hydroelectric stations, wind power stations, pulverized coal stations and a natural gas combined cycle, while hypothesized power plants with new technologies include solar stations, wind power stations, pulverized coal stations, a natural gas combined cycle and an integrated gasification combined cycle with and without capture and sequestration. The proposed methodology includes financial risk management in the framework of a two stage stochastic programme for energy planning under uncertainty: demands and fuel price. A deterministic mixed integer linear programming formulation is extended to a two-stage stochastic programming model in order to take into account random parameters, which have discrete and finite probabilistic distributions. Thus, the expected value of the total costs of power generation is minimized, while the objective of carbon emission reduction is achieved. Furthermore, conditional value at risk (CVaR), a most preferable risk measure in the financial risk management, is incorporated within the framework of two-stage mixed integer programming. The mathematical formulation, which is called mean-risk model, is applied for the purpose of minimizing expected value. The process is formulated as a mixed integer linear programming model, implemented in GAMS (General Algebraic Modeling System) and solved using the CPLEX algorithm, a commercial solver embedded in GAMS. The computational results demonstrate the effectiveness of the proposed new methodology. The optimization model is applied to an existing Ontario Power Generation (OPG) fleet. Four planning scenarios are considered: a base load demand, a 1.0% growth rate in demand, a 5.0% growth rate in demand, a 10% growth rate in demand and a 20% growth rate in demand. A sensitivity analysis study is accomplished in order to investigate the effect of parameter uncertainties, such as uncertain factors on coal price and natural gas price. The optimization results demonstrate how to achieve the carbon emission mitigation goal with and without new technologies, while minimizing costs affects the configuration of the OPG fleet in terms of generation mix, capacity mix and optimal configuration. The selected new technologies are assessed in order to determine the risks of investment. Electricity costs with new technologies are lower than with the existing technologies. 60% CO2 reduction can be achieved at 20% growth in base load demand with new technologies. The total cost of electricity increases as we increase CO2 reduction or increase electricity demand. However, there is no significant change in CO2 reduction cost when CO2 reduction increases with new technologies. Total cost of electricity increases when fuel price increases. The total cost of electricity increases with financial risk management in order to lower the risk. Therefore, more electricity is produced for the industry to be on the safe side.