Multi-Objective Nonlinear Programming Model for Reducing Octane Number Loss in Gasoline Refining Process Based on Data Mining Technology

Abstract

To simultaneously reduce automobile exhaust pollution to the environment and satisfy the demand for high-quality gasoline, the treatment of fluid catalytic cracking (FCC) gasoline is urgently needed to minimize octane number (RON) loss. We presented a new systematic method for determining an optimal operation scheme for minimising RON loss and operational risks. Firstly, many data were collected and preprocessed. Then, grey correlative degree analysis and Pearson correlation analysis were used to reduce the dimensionality, and the major variables with representativeness and independence were selected from the 367 variables. Then, the RON and sulfur (S) content were predicted by multiple nonlinear regression. A multi-objective nonlinear optimization model was established with the maximum reduction in RON loss and minimum operational risk as the objective function. Finally, the optimal operation scheme of the operating variable corresponding to the sample with a RON loss reduction greater than 30% in 325 samples was solved in Python.

On Min-Max Goal Programming Approach for Solving Piecewise Quadratic Fuzzy Multi- Objective De Novo Programming Problems

“…In the case for existing the supremum, there is (𝑢 1𝑗 , … , 𝑢 𝑃𝑗 ) ∈ C ̂ and (𝑣 1𝑗 , … , 𝑣 𝑄𝑗 ) ∈ D ̂ with 4) and ( 8), we conclude that x * ∈ X is an α −parametric efficient solution for Problem (6).…”

“…Where x * ∈ X is said to be an α −efficient solution for Problem (2) if and only if x * ∈ X is an α −parametric efficient solution for Problem (6).…”

“…Proof: Necessity: Let x * ∈ X be an α −efficient solution for Problem (2) and x * ∈ X be not an α −n parametric efficient solution for Problem (6). Then there are x 1 ∈ X, s pj ∈ χ pj α and t qj ∈ χ qj α , p = 1, P; q = 1, Q; j = 1, n. such that Since, s pj ∈ χ pj α and t qj ∈ χ qj α , p = 1, P; q = 1, Q; j = 1, n. we have Contradiction the assumption that x * ∈ X be an α −efficient solution for Problem (2).…”

“…Ahmed [5] proposed a method to solve MO problems with intuitionistic fuzzy parameters. Liu et al [6] introduced a new systematic method for determining an optimal operation scheme for minimizing octane number loss and operational risks.…”

Reducing Octane Number Loss in Gasoline Refining Process by Using the Improved Sparrow Search Algorithm

“…However, this method has high instruments requirements and is difficult to perform in practice. The mean octane number was predicted by Liu et al [6] through the establishment of a multi-objective nonlinear optimization model with maximum RON loss reduction and minimum operational risk. Wang et al [7] used the partial least squares regression method to fit the analysis of gasoline octane number.…”