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Abstract: Mobile cranes represent conventional construction machinery that is indispensable for the erection of most prefabricated buildings, especially those containing heavy components. However, it is also common knowledge that the engagement of these machines has a significant influence on the environment, various social aspects of the construction process, and its economic benefits. Optimal positioning of the mobile crane on the construction site, primarily driven by the contractor’s interest to perform assembly operations with expensive machinery as effectively as possible, considerably reduces not only the costs of engaging such a machine but indirectly also its negative impacts on construction sustainability. This paper discusses an exact nonlinear model for the optimization task. The optimization model consists of a cost objective function that is subject to various duration and positioning constraints for the mobile crane, including bounds on its degrees of freedom of movement and stop positions. Because the model formulation includes discontinuous and non-smooth expressions, nonlinear programming with discontinuous derivatives (DNLP) was employed to ensure the optimal solution was reached. The model provides the mobile crane operator with exact key information that enables the complete and optimal assembly of the building structure under consideration. Additionally, the information gained on the optimal distribution of the mobile crane rental period to assembly operations allows for a detailed duration analysis of the entire process of building structure erection, which can be used for its further improvement. An application example is given in this study to demonstrate the advantages of the proposed approach.
Keywords: construction sustainability, mobile crane, nonlinear programming with discontinuous derivatives, optimization, positioning
Published in DKUM: 18.12.2023; Views: 403; Downloads: 19
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Abstract: The optimization for symmetrical gravity retaining walls of different heights is examined in this study. For this purpose, an optimization problem of continuous functions is developed. The continuous functions are the objective function defined as the cross-sectional area of the wall and the constraint functions derived from external stability and internal stability verifications. The verifications are listed as the overturning, the forward sliding, the bearing capacity, the shears in the stem and the bendings in the stem. The heights of the walls are selected as 2.0, 3.0, and 4.0 m in order to investigate the outline of the optimum cross-section and the effect of the wall height on the outline. Additionally, the physical and mechanical properties of the soil are kept constant in order to compare only the effect of the height on the geometry. The upper and lower bounds of the solution space are specified to be as wide as possible and the minimum dimensions suggested for the gravity retaining walls are not taken into account. A common feature of the optimum cross-sections of walls with different heights is to have a very wide lower part like a wall foundation and a slender stem. However, other than the forward sliding constraint, the bending constraints are active at the optimum values of the variables.
Keywords: gravity retaining wall, nonlinear optimization, continuous variables, interior point method
Published in DKUM: 14.06.2018; Views: 1575; Downloads: 200
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Abstract: This paper presents the cost optimal project scheduling. The optimization was performed by the nonlinear programming approach, NLP The nonlinear total project cost objective function is subjected to the rigorous system of the activity preceden- ce relationship constraints, the activity duration constraints and the project duration constraints. The set of activity precedence relationship constraints was defined to comprise Finish-to-Start, Start-to-Start, Start-to-Finish and Finish-to-Finish precedence relationships between activities. The activity duration constraints determine relationships between minimum, maximum and possible duration of the project activities. The project duration constraints define the maximum feasible project duration. A numerical example is presented at the end of the paper in order to present the applicability of the proposed approach.
Keywords: project management, scheduling, optimization, nonlinear programming, NLP
Published in DKUM: 10.07.2015; Views: 1863; Downloads: 418
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Abstract: Fuel-cell efficiencies yield substantial reductions in the emissions of climate-change gases and promise an end to exclusive reliance on carbon fuels for energy. Fuel cells, CO2 reuse, process heat integration, and open gas turbine electricity cogeneration can be optimized simultaneously, using a nonlinear programming (NLP) algorithm. The simplified NLP model contains equations of structural and parametric optimization. This NLP model is used tooptimize complex and energy-intensive continuous processes. This procedure does not guarantee a global cost optimum, but it does lead to good, perhaps near-optimum, designs. The plant, which produces methanol, has a surplus of hydrogen (H2) and CO2 flow rates in purge gas. H2 is separated from the purge gas by an existing pressure swing adsorption (PSA) column. Pure H2 can be usedas fuel in fuel cells. CO2 can be separated from the outlet stream (purge gas) by a membrane or absorption system (absorber and regenerator) or an adsorption system and reused as a reactant in a reactor system. Therefore, theproduct yield can be increased and CO2 emissions can be reduced, simultaneously. CO2 emissions can then be reduced at the source. The retrofitted process can be operated within existing parameters. Using a methanol process as a case study, the CO2 emission flow rate can be reduced by4800 t/a. The additional electricity cogeneration in the gas turbine and in fuel cells and additional flow rates of the raw material could generate an additional profit of 2.54 MEUR/a.
Keywords: chemical processing, methanol production, optimization, nonlinear programming, CO2 reuse, fuel cells, heat integration, energy cogeneration
Published in DKUM: 31.05.2012; Views: 2763; Downloads: 122
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Abstract: The opportunities for additional profit depend very much on the existing plant and energy system. Heat and power integration can reduce fuel usage in chemical processes. Nonlinear programming contains equations which enable structural and parametric optimization. The NLP model is formulated using an optimum energy target of process integration and electricity generation using a gas turbine with separator. The reactor acts as a combustion chamber of the gas turbine plant, producing a lot of energy. The simultaneous NLP approach can account for capital cost, integration of combined heat and power, process modification and additional production of trade-offs, and can thus yield a better solution. The combined production of electricity, heat and chemical products can lead to better process efficiency. The methanol plant was optimized using a mathematical nonlinear programming model by including an additional flowrate of hydrogen in crude methanol recycle and increasing the methanol production by 2,5%. The electricity can be generated in methanol recycle using a gas turbine. The total additional profit is 2,5 MEUR/a.
Keywords: chemical engineering, methanol production, simultaneous process optimization, nonlinear programming, cogeneration, product increase
Published in DKUM: 30.05.2012; Views: 2644; Downloads: 50
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