Genetic Algorithm Based Optimization Procedure Research Articles

Kinetic Modeling of the Devolatilization of Pulverized Coal, Poplar Wood, and Their Blends in a Thermogravimetric Analyzer and a Flat Flame Reactor.

Devolatilization kinetics of coal, poplar wood, and blends containing 10 and 20 wt % of biomass were characterized. Measurements were carried out under inert atmosphere with heating rates between 10 K min-1 and ∼106 K s-1 using a thermogravimetric analyzer (TGA) and a flat flame reactor (FFR). Measured data were simulated using the chemical percolation devolatilization (CPD) model and a global kinetic scheme based on two competitive reactions integrating a refined differential reaction model. The CPD model failed to simulate TGA results but reproduced FFR data relatively well. As for the global model, selecting kinetic parameters from the literature turned out to lead to unsuitable predictions. Fitted values of the activation energies Ea,i, pre-exponential factors Ai, mass stoichiometric coefficients Yi, and the reaction model factor n were therefore inferred using a genetic algorithm-based optimization procedure, leading to obtain an excellent agreement between simulated and measured data. The assessed Ea,i values were found to be lower for wood than for coal, which is consistent with the higher energy required to break the strong C-C bonds holding the highly cross-linked aromatic structures of coal. Besides, blending coal with 20 wt % of wood induced a decrease of Ea,i values, which went from 99.79 to 86.1 kJ mol-1 and from 186.72 to 171.57 kJ mol-1 for the first and second reactions prevailing at low and high temperatures, respectively. Finally, the fact that the activation energy of the first devolatilization reaction was found to be lower with the blend containing 20% of wood than for wood illustrated the probable existence of synergies, as also exemplified by the characteristic devolatilization times for blended samples, which were found to be relatively similar to and even lower than that of wood.

Biocode: A Data-Driven Procedure to Learn the Growth of Biological Networks.

Probabilistic biological network growth models have been utilized for many tasks including but not limited to capturing mechanism and dynamics of biological growth activities, null model representation, capturing anomalies, etc. Well-known examples of these probabilistic models are Kronecker model, preferential attachment model, and duplication-based model. However, we should frequently keep developing new models to better fit and explain the observed network features while new networks are being observed. Additionally, it is difficult to develop a growth model each time we study a new network. In this paper, we propose Biocode, a framework to automatically discover novel biological growth models matching user-specified graph attributes in directed and undirected biological graphs. Biocode designs a basic set of instructions which are common enough to model a number of well-known biological graph growth models. We combine such instruction-wise representation with a genetic algorithm based optimization procedure to encode models for various biological networks. We mainly evaluate the performance of Biocode in discovering models for biological collaboration networks, gene regulatory networks, and protein interaction networks which features such as assortativity, clustering coefficient, degree distribution closely match with the true ones in the corresponding real biological networks.

Acid hydrolysis-based sugarcane bagasse biorefining for levulinic acid production: Dynamic mechanistic modeling under varying operating conditions

The study on the lignocellulosic material conversion into bio-based platform chemicals, such as levulinic acid (LA), is one of the most promising routes to promote the development of advanced biorefineries. In this work, a dynamic mechanistic model is developed to simulate the LA production from lignocellulosic material. A wide operating range is used to estimate the parameters of the reaction kinetics. Because multi-parameter estimation problem is complex, a genetic algorithm-based optimization procedure is used to determine the optimum parameters values. Measurements are obtained for various reaction times (0 - 45 min) temperatures (150 – 200 °C) and acid concentration of 7.0 % w/v H2SO4. The calculated reaction rates for the state variables, concentrations of LA, glucose, 5-hydroxymethylfurfural and humins are used to construct the dynamic mechanistic model. The prediction of measured state variables was particularly accurate, as determined by the root mean square error (RMSE) and correlation coefficient (R2). Therefore, a satisfactory agreement between experimental LA yield of 57.2 mol% and computed LA yield of 56.4 mol% was achieved (at 200 °C, 7.0 % w/v H2SO4, 45 min). The proposed methodology drives the systematic development of an industrially reliable dynamic mechanistic model for LA production from sugarcane bagasse as a means to increase the LA yields in the biorefinery.

Optimization of maintenance strategies for railway track-bed considering probabilistic degradation models and different reliability levels

An optimization-based maintenance scheduling framework is an essential tool to plan the necessary investment to maintain the required performance of a railway line. In the present study, a methodology is proposed to minimize the present value of the life cycle maintenance costs and maximize the life cycle quality level of the track-bed considering different levels of reliability. Probabilistic degradation models are developed for predicting the evolution of the railway track condition over time. Afterwards, a Genetic Algorithm based optimization procedure is applied for obtaining a set of optimal solutions taking into account several constrains. The proposed methodology is applied to an Italian railway track-line case study. The results show that it is possible to develop a decision support system to help railway managers to schedule railway track maintenance operations based on the optimal trade-off between maintenance costs and railway track geometry condition for different levels of reliability.

Flexibility Exchange Strategy to Facilitate Congestion and Voltage Profile Management in Power Networks

This paper proposes a novel flexibility exchange strategy to facilitate the management of congestion issues and voltage profiles (e.g., avoiding voltage violation and reducing voltage fluctuation) via minimum participation from customers or aggregators. In the proposed approach, the expectation of voltage profiles and power flow is determined by network constraints and customers’ requirement, and it is used to guide the estimation of network state toward the expected state so that the predefined expectation (regarding voltage profile and power flow) is fulfilled. Availability of flexibility exchange from customers is integrated in estimation process. Flexibility factors are proposed to constrain the variation of network variables including voltage, power consumption/generation and power flow. A genetic algorithm based-optimisation procedure is applied to obtain the minimum power variation from customers (i.e., minimum power variation from customers) while the defined expectation and constraints of flexibility availability are met. The approach is tested out on two representative distribution networks and the results have demonstrated the feasibility of the proposed approach in obtaining optimal flexibility exchange strategy that meets the predefined requirement/expectation whilst involving the least power variation from customers.

Optimizing Bridge Network Retrofit Planning Based on Cost-Benefit Evaluation and Multi-Attribute Utility Associated with Sustainability

During their service life, bridge networks may be exposed to extreme events, including strong earthquakes, which pose an imminent threat to society, economy, and surrounding environment. This threat reinforces the need to implement updated sustainability assessment and optimal risk mitigation procedures. The sustainability-based seismic optimization of bridge networks considering the utility associated with cost and benefit of retrofit interventions is investigated. The ultimate aim of this framework is to reduce the extent of earthquake damage to society, economy, and environment, while simultaneously minimizing the total retrofit costs of bridge networks. The total benefit of a retrofit plan is quantified in terms of the reduction in the seismic loss during a given time interval using multi-attribute utility theory. Retrofit actions associated with varying improvement levels are considered herein. A genetic algorithm based optimization procedure is adopted to determine the optimal retrofit action for each bridge within an existing bridge network.

Dynamics of polymer and polymer composite rotors – An operator based finite element approach

This paper presents a differential time operator based approach to obtain equations of motion of a generally viscoelastic rotor–shaft after discretizing the shaft continuum with finite beam elements. The equations of motion are utilized to study dynamic behaviour of a generic viscoelastic composite rotor–shaft comprising of viscoelastic reinforcing fibres in a viscoelastic matrix. Anelastic Displacement Field model (ADF), a time-domain material model, is used to represent the viscoelastic constitutive relationships. The model parameters are extracted by a Genetic Algorithm based optimisation procedure from frequency-dependent values of storage and loss modulii. For an example the composite rotor is assumed to be made by reinforcing unidirectional long graphite fibres in a PVC (Poly-Vinyl-Chloride) matrix. The equations of motion are used for numerical simulation as well as comparison of stability limit of spin speed and unbalance response amplitude at the disc of two rotor–shaft systems, one made of pure PVC and the other made by reinforcing graphite fibres in the PVC matrix. It is concluded that reinforcement of long fibre, enhances the stability limit of spin speed as well as the first natural frequency in comparison with those of rotor–shaft made of pure PVC.

Lessons from nature: computational design of biomimetic compounds and processes.

Through millions of years of evolution, Nature has accomplished the development of highly efficient and sustainable processes and the idea to understand and copy natural strategies is therefore very appealing. However, in spite of intense experimental and computational research, it has turned out to be a difficult task to design efficient biomimetic systems. Here we discuss a novel strategy for the computational design of biomimetic compounds and processes that consists of i) target selection; ii) atomistic and electronic characterization of the wild type system and the biomimetic compounds; iii) identification of key descriptors through feature selection iv) choice of biomimetic template and v) efficient search of chemical and sequence space for optimization of the biomimetic system. As a proof-of-principles study, this general approach is illustrated for the computational design of a 'green' catalyst mimicking the action of the zinc metalloenzyme Human Carbonic Anhydrase (HCA). HCA is a natural model for CO2 fixation since the enzyme is able to convert CO2 into bicarbonate. Very recently, a weakly active HCA mimic based on a trihelical peptide bundle was synthetized. We have used quantum mechanical/molecular mechanical (QM/MM) Car-Parrinello simulations to study the mechanisms of action of HCA and its peptidic mimic and employed the obtained information to guide the design of improved biomimetic analogues. Applying a genetic algorithm based optimization procedure, we were able to re-engineer and optimize the biomimetic system towards its natural counter part. In a second example, we discuss a similar strategy for the design of biomimetic sensitizers for use in dye-sensitized solar cells.

Combined loading of caisson foundations in cohesive soil: Finite element versus Winkler modeling

The undrained response of massive caisson foundations to combined horizontal, vertical and moment loading is parametrically investigated through a series of 3D finite element analyses. The parameters are: (a) the embedment ratio (D/B), (b) the factor of safety against initial vertical loading (FSV) and (c) the ratio of the overturning moment to the horizontal force applied at the top of the caisson (M/Q). Emphasis is given on: (i) the identification of all possible failure mechanisms in M–Q–N space, (ii) the developed stress distributions along the caisson walls for various load levels up to complete failure conditions. The results are then used as a feedback for calibrating the parameters of a generalized four-type spring model, originally proposed by Gerolymos and Gazetas (2006), through a genetic algorithm-based optimization procedure. The predictions of the Winkler model compare very well with the FE results, not only at the local response level (in terms of stress distributions along the caisson shafts), but at a global response level (in terms of force–displacement curves and M–Q–N failure envelopes at the top of the caisson) as well. Contrary to established lateral soil resistance theories, it is shown that both the ultimate horizontal soil reaction and resisting moment per unit depth do not solely depend on the strength properties of soil and geometry of the caisson but are also functions of the applied load ratio M/Q and initial soil yielding due to vertical loading. Interesting conclusions are also drawn regarding the transition from the elastic to the ultimate limit state (hardening). Quantifying through analytical expressions the contribution of each of the two basic lateral resisting mechanisms to the response of the caisson, a classification method for embedded foundations is then proposed. The capabilities of the Winkler model are further demonstrated through comparison with FE analysis of the caisson cyclic lateral response.

A route for efficient non-resonance cloaking by using multilayer dielectric coating

An approach for designing transmission cloaks by using ordinary dielectrics instead of meta- and plasmonic materials is proposed and demonstrated by the development of a multi-layer cloak for hiding cylindrical objects larger than the wavelengths of incident radiation. The parameters of the cloak layers were found by using the Genetic Algorithm-based optimization procedure, which employed the reciprocal of total scattering cross width of the cloaked target, derived from the solution of the Helmholtz equation, as the fitness function. The proposed cloak demonstrated better cloaking efficiency than did a similarly sized metamaterial cloak designed by using the transformation optics relations.

Kinetic modeling of high temperature oxidation of Ni-base alloys

The rates of isothermal and cyclic oxidation and the elemental concentration profiles as a function of time of oxidation for a few Ni-base superalloys were determined through a modified Wagner’s oxidation model and the solution of coupled elemental diffusion equations. Thermodynamically calculated interfacial elemental concentrations and oxygen partial pressures for the multi-component Ni-base alloys were used as boundary conditions for the solution of Wagner’s equation and the elemental coupled diffusion equations (for Cr, Al and O). The multiple elemental diffusion and mass conservation equations were solved using a numerical procedure. The dependence of self/tracer-diffusivities of Cr, Al and O in the corundum phase on the oxygen partial pressures was deduced using a genetic algorithm based optimization procedure incorporating the experimental parabolic rate constants for several Ni-base alloys. Rates of cyclic oxidation were then deduced from the deterministic interfacial cyclic oxidation spalling model (DICOSM) developed by Smialek [1]. The calculated oxidation rates were in reasonable agreement with the experimental values for a range of multi-component Ni-base alloys.

Electro-thermal battery model identification for automotive applications

This paper describes a model identification procedure for identifying an electro-thermal model of lithium ion batteries used in automotive applications. The dynamic model structure adopted is based on an equivalent circuit model whose parameters are scheduled on the state-of-charge, temperature, and current direction. Linear spline functions are used as the functional form for the parametric dependence. The model identified in this way is valid inside a large range of temperatures and state-of-charge, so that the resulting model can be used for automotive applications such as on-board estimation of the state-of-charge and state-of-health. The model coefficients are identified using a multiple step genetic algorithm based optimization procedure designed for large scale optimization problems. The validity of the procedure is demonstrated experimentally for an A123 lithium ion iron-phosphate battery.

Optimal Heat Distribution Among Discrete Protruding Heat Sources in a Vertical Duct: A Combined Numerical and Experimental Study

This paper reports the results of experimental and numerical investigations of optimal heat distribution among the protruding heat sources under laminar conjugate mixed convection heat transfer in a vertical duct. A printed circuit board with 15 heat sources forms a wall of a duct. Three-dimensional governing equations of flow and heat transfer were solved in the flow domain along with the energy equation in the solid domain using FLUENT 6.3. A database of temperatures of each of the heat sources for different heat distributions is generated numerically. Artificial neural networks (ANNs) were used as a forward model to replace the time consuming complex computational fluid dynamics (CFD) simulations. The functional relationship between heat input distribution and the corresponding temperatures of the heat sources obtained by training the network is used to drive a genetic algorithm based optimization procedure to determine the optimal heat distribution. The optimal distribution here refers to the apportioning of a fixed quantity of heat among 15 heat sources, keeping the maximum of the temperatures of the heat sources to a minimum. Furthermore, the heat distribution corresponding to a set of specified target temperatures of the heat sources is obtained using a network that is trained and tested with a database of temperatures of the heat sources generated using FLUENT 6.3 in the range of total heat dissipation of 5–25 W. Using this network, it was possible to maximize the total heat dissipation from the heat sources for a given target temperature directly. In order to validate the optimization method, a low speed vertical wind tunnel has been used to carry out the mixed convection experiments for different combinations of heat distribution and also for the optimal heat distribution, and the temperatures of the heat sources were measured. The results of the numerical simulations, ANN, and the corresponding experimental results are in good agreement.

Maintenance scheduling in manufacturing systems based on predicted machine degradation

In this paper, we propose a new method for scheduling of maintenance operations in a manufacturing system using the continuous assessment and prediction of the level of performance degradation of manufacturing equipment, as well as the complex interaction between the production process and maintenance operations. Effects of any maintenance schedule are evaluated through a discrete-event simulation that utilizes predicted probabilities of machine failures in the manufacturing system, where predicted probabilities of failure are assumed to be available either from historical equipment reliability information or based on the newly available predictive algorithms. A Genetic Algorithm based optimization procedure is used to search for the most cost-effective maintenance schedule, considering both production gains and maintenance expenses. The algorithm is implemented in a simulated environment and benchmarked against several traditional maintenance strategies, such as corrective maintenance, scheduled maintenance and condition-based maintenance. In all cases that were studied, application of the newly proposed maintenance scheduling tool resulted in a noticeable increase in the cost-benefits, which indicates that the use of predictive information about equipment performance through the newly proposed maintenance scheduling method could result in significant gains obtained by optimal maintenance scheduling.

Conjunctive Use of Models to Design Cost-Effective Ozone Control Strategies

The management of tropospheric ozone (O3) is particularly difficult. The formulation of emission control strategies requires considerable information including: (1) emission inventories, (2) available control technologies, (3) meteorological data for critical design episodes, and (4) computer models that simulate atmospheric transport and chemistry. The simultaneous consideration of this information during control strategy design can be exceedingly difficult for a decision-maker. Traditional management approaches do not explicitly address cost minimization. This study presents a new approach for designing air quality management strategies; a simple air quality model is used conjunctively with a complex air quality model to obtain low-cost management strategies. A simple air quality model is used to identify potentially good solutions, and two heuristic methods are used to identify cost-effective control strategies using only a small number of simple air quality model simulations. Subsequently, the resulting strategies are verified and refined using a complex air quality model. The use of this approach may greatly reduce the number of complex air quality model runs that are required. An important component of this heuristic design framework is the use of the simple air quality model as a screening and exploratory tool. To achieve similar results with the simple and complex air quality models, it may be necessary to “tweak” or calibrate the simple model. A genetic algorithm-based optimization procedure is used to automate this tweaking process. These methods are demonstrated to be computationally practical using two realistic case studies, which are based on data from a metropolitan region in the United States.

Design of GFRP deck and deck-to-girder connections for girder bridges

This paper presents the design and analysis of GFRP (glass fiber reinforced polymer) decks for the girder bridges. In the material design, Eglass fibers with vinyl ester resin are assumed for the materials. Five GFRP plates having different stacking patterns and material compositions are designed and pultruded. Total 100 specimens are tested for tension, compression, shear, and flexure. Based on the material properties determined in the material test, structural shape of deck profile having multi-cellular tube section is proposed. The proposed profile is finally optimized by using a genetic algorithm-based optimization procedure. Based on the results of this study, viable GFRP patterns and cross-sectional dimensions of deck profile with deck-to-girder connections suitable for the deck renewal projects are proposed. Using the proposed cross-sectional shape and GFRP patterns, a design of GFRP deck for the typical steel I-girder bridge is also presented in this paper.

An optimization design procedure for fiber reinforced polymer web-core sandwich bridge deck systems

The development of optimization design procedures has emerged as an essential component primarily to overcome the challenge of high initial construction costs associated with the fiber reinforced polymer (FRP) bridge deck systems. This paper devises a genetic algorithm-based optimization procedure to minimize the weight of FRP web core sandwich bridge deck systems. A Ritz-based simplified analysis method is applied to couple with the proposed optimization procedure as the structural analysis engine. The design procedure involves the parallel optimal search of mixed continuous and discrete parameters––geometrical parameters, sandwich configuration and material architecture––for the whole sandwich structure. A novel data structure is proposed for the genetic representation in a one-gene-one-variable format. Consequently, specific genetic operators are tailored to accommodate the nonstandard data structure. A problem-independent scheme that does not require any penalty parameter is developed to handle the design constraints. The resulting design procedure provides an effective optimal design framework for the preliminary design stage before performing detailed finite element analysis.

Evaluation of genetic operators and solution representations for shape recognition by genetic algorithms

In this paper, we investigate the genetic algorithm based optimization procedure for structural pattern recognition in a model-based recognition system using attributed relational graph matching technique. In this study, potential solutions indicating the mapping between scene and model vertices are represented by integer strings. The test scene may contain multiple occurrences of different or the same model object. Khoo and Suganthan [Proc. IEEE Congr. Evolutionary Comput. Conf. 2001, p. 727] proposed a solution string representation scheme for multiple mapping between a test scene and all model objects and with the uniform crossover operator. In this paper, we evaluate this proposed solution string representation scheme with another representation scheme commonly used to solve the problem. In addition, a comparison between the uniform, one-point and two-point crossover operators was made. An efficient pose-clustering algorithm is used to eliminate any wrong mappings and to determine the presence/pose of the model in the scene. Simulations are carried out to evaluate the various solution representations and genetic operators.

Fuzzy multicriterion design using immune network simulation

Structural optimization problems have been traditionally formulated in terms of crisply defined objective and constraint functions. With a shift in application focus towards more practical problems, there is a need to incorporate fuzzy or noncrisp information into an optimization problem statement. Such practical design problems often deal with the allocation of resources to satisfy multiple, and frequently conflicting design objectives. The present paper deals with a genetic algorithm based optimization procedure for solving multicriterion design problems where the objective or constraint functions may not be crisply defined. The approach uses a genetic algorithm based simulation of the biological immune system to solve the multicriterion design problem; fuzzy set theory is adopted to incorporate imprecisely defined information into the problem statement. A notable strength of the proposed approach is its ability to generate a Pareto-Edgeworth front of compromise solutions in a single execution of the GA.

Toward a heuristic optimum design of rolling schedules for tandem cold rolling mills

Scheduling for tandem cold mills refers to the determination of inter-stand gauges, tensions and speeds of a specified product. Optimal schedules should result in maximized throughput and minimized operating cost. This paper presents a genetic algorithm based optimization procedure for the scheduling of tandem cold rolling mills. The optimization procedure initiates searching from a logical staring point — an empirical rolling schedule — and ends with an optimum cost. Cost functions are constructed to heuristically direct the genetic algorithm’s searching, based on the consideration of power distribution, tension, strip flatness and rolling constraints. Numerical experiments have shown that the proposed method is more promising than those based on semi-empirical formulae. The results generated from a case study show that the proposed approach could significantly improve empirically derived settings for the tandem cold rolling mills.