(85g) A New Approach to Optimizing Facility Layout by Mapping Risk On Grids

Facility layout is usually conducted for evaluating the location of process plant buildings with respect to existing equipment in the plant. Optimal siting can be achieved at the planning and design stages which can help saving capital costs rather than changing locations in a later stage after it has been built taking into account costs of land and piping versus potential damage. This research is focused on finding an optimal placement for hazardous facilities and other process plant buildings using optimization theory and mapping risks on the given land in order to calculate risk in financial terms. Incidents could have possible consequences such as jet fire, pool fire, VCE and BLEVE from flammable gas release of hazardous facilities, and the probability of each can be derived from initial leak hole size estimation through event tree analysis. The given land is divided in a square lattice or grid of which the sides have a certain size and in which each square acquires a risk-score. These risk-scores such as the probability of structural damage, are to be multiplied by a weighing factor depending on type of structure and price of potential facilities which would be built on the lattice. Finally this will give us the financial risk. Accompanying by the suggested safety concepts, the new model takes into account construction and operational costs. The overall cost of locations is a function of piping cost, management cost, land cost, and financial risk. The proposed approach is a mixed integer optimization problem (MIP) that identifies attractive locations by minimizing overall cost. This approach gives the coordinates of the best location of each facility in a 2-D plane, and estimates the total piping length, and the amount of occupied land. In order to show the applicability of the proposed plan, a case study is carried out for a propylene production plant planned in a certain location by taking account of real meteorological data of the affected area.