Doaa Ahmed Abd El-wahab Hammad
Asst. Professor
A NEW TREATMENT SOLUTION OF INTERVAL NONLINEAR PROGRAMMING PROBLEMS: A CASE STUDY OF GREEN FUEL PRODUCTION
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) • 2023
Publication Information
Authors
M. A. ELSISY; D. A. HAMMAD; MARWA M ABDEL-ATY; HAMED SHAWKY ZIED;M. A. EL-SHORBAGY
Keywords
Interval Nonlinear Programming Problem, KKT Conditions, Stability Set Of First Kind, Genetic
Algorithm, Particle Swarm Optimization, Firefly Algorithm, Green Fuel Production, Biogas Production
Process
Journal
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition)
Publisher
University of China
Volume
42
Issue
1671-5497
Pages
47-76
publication.type
International
Paper Link
Open Link
Supplementary Materials
Doaa Ahmed Abd El-wahab Hammad_4.JJU.pdf
Abstract
Green fuel is growing in popularity in recent years. Bio-reactive waste converted to green fuel through
anaerobic digestion technology. The performance of biogas unit has been optimized and formulated as
interval programming problems as function of inlet feed rate, retention time fermentation temperature and
pH. A new treatment for solving the interval nonlinear programming problem (INPP) is discussed. All the
intervals in the INPP are replaced by new variables. This the modified nonlinear programming problem
(MIPP). We presented three hybrid evolutionary algorithms (EAs) which are chaotic genetic algorithm
(CGA), chaotic particle swarm optimization (CPSO) and chaotic firefly algorithm (CFA) to solve MIPP. The
Karush–Kuhn–Tucker (KKT) conditions for MIPP are gotten. These equations are solved as algebraic
equations. Its solutions may be represented as a function of new variables to get the stability set of first
kind. The staring points in EAs is gotten by the Newton method. Finally, the comparison between the
stability set of first kind, CGA, CPSO and CFA are presented with discussion. An empirical optimization
model of biogas production has been constructed with accuracy of 90%.
anaerobic digestion technology. The performance of biogas unit has been optimized and formulated as
interval programming problems as function of inlet feed rate, retention time fermentation temperature and
pH. A new treatment for solving the interval nonlinear programming problem (INPP) is discussed. All the
intervals in the INPP are replaced by new variables. This the modified nonlinear programming problem
(MIPP). We presented three hybrid evolutionary algorithms (EAs) which are chaotic genetic algorithm
(CGA), chaotic particle swarm optimization (CPSO) and chaotic firefly algorithm (CFA) to solve MIPP. The
Karush–Kuhn–Tucker (KKT) conditions for MIPP are gotten. These equations are solved as algebraic
equations. Its solutions may be represented as a function of new variables to get the stability set of first
kind. The staring points in EAs is gotten by the Newton method. Finally, the comparison between the
stability set of first kind, CGA, CPSO and CFA are presented with discussion. An empirical optimization
model of biogas production has been constructed with accuracy of 90%.