It is hardly possible to diminish importance of the key roles of petroleum refining in modern chemical industry. It
produces different types of fuels (e.g. gasoline, diesel, furnace fuel, etc.) or wide variety of valuable chemicals
which constitutes significant part of global market. Due to its importance, optimization of refining processes is
essential. Capacities of modern units are high, and hence, even small performance improvements might lead to
significant economical profits.
Conventional methods of optimization for many years had been based on formulation of single objective function
and search of its minimum (or maximum). In case of complex industrial processes either most important objective
was chosen or single objective function in some way related to economic effect (e.g., profit maximization or cost
minimization). Practically solution of such optimization problem yields single-point solution. This approach has
some obvious disadvantages. Optimization of only one objective while disregarding the others might lose some
practically meaningful solution and at times solution may be practically irrelevant. Nevertheless, sometimes relation
between real objectives and their economic effect is not clear which makes difficult formulation of single objective
function. Moreover, cost or profit functions are site-specific and time-specific and solution may not be useful.
Multi-objective optimization with its concepts and methods allow overcoming issues mentioned above. Applying
multi-objective approach for solving real-life optimization problems, it becomes possible to take into account all of
desired objective functions and treat them directly regardless of any explicit relation to economic efficiency. It is
especially important for petroleum refining processes due to its complexity, i.e. variety of components in feedstock
and products, diversity of chemical reactions, number of units included into processing scheme. Such nature of oil
refining processes makes multi-objective optimization “a more advanced” tool in a search of optimal solution(s).
It is hardly possible to diminish importance of the key roles of petroleum refining in modern chemical industry. It
produces different types of fuels (e.g. gasoline, diesel, furnace fuel, etc.) or wide variety of valuable chemicals
which constitutes significant part of global market. Due to its importance, optimization of refining processes is
essential. Capacities of modern units are high, and hence, even small performance improvements might lead to
significant economical profits.
Conventional methods of optimization for many years had been based on formulation of single objective function
and search of its minimum (or maximum). In case of complex industrial processes either most important objective
was chosen or single objective function in some way related to economic effect (e.g., profit maximization or cost
minimization). Practically solution of such optimization problem yields single-point solution. This approach has
some obvious disadvantages. Optimization of only one objective while disregarding the others might lose some
practically meaningful solution and at times solution may be practically irrelevant. Nevertheless, sometimes relation
between real objectives and their economic effect is not clear which makes difficult formulation of single objective
function. Moreover, cost or profit functions are site-specific and time-specific and solution may not be useful.
Multi-objective optimization with its concepts and methods allow overcoming issues mentioned above. Applying
multi-objective approach for solving real-life optimization problems, it becomes possible to take into account all of
desired objective functions and treat them directly regardless of any explicit relation to economic efficiency. It is
especially important for petroleum refining processes due to its complexity, i.e. variety of components in feedstock
and products, diversity of chemical reactions, number of units included into processing scheme. Such nature of oil
refining processes makes multi-objective optimization “a more advanced” tool in a search of optimal solution(s).
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