B. V. Babu

New optimization techniques in engineering

Chapter 2: An Introduction to Genetic Algorithms for Engineering Applications Chapter 3: Memetic Algorithms Chapter 4: Scatter Search and Path Relinking: Foundations and Advanced Designs Chapter 5: Ant Colony Optimization Chapter 6: Differential Evolution Chapter 7: SOMA-Self-Organizing Migrating Algorithm Chapter 8: Discrete Particle Swarm Optimization:Illustrated by the Traveling Salesman Problem

Modified differential evolution (MDE) for optimization of non-linear chemical processes

In recent years, evolutionary algorithms (EAs) are gaining popularity for finding the optimal solution of non-linear multimodal problems encountered in many engineering disciplines. Differential evolution (DE), one of the evolutionary algorithms, is a novel optimization method capable of handling nondifferentiable, non-linear and multimodal objective functions. Previous studies have shown that differential evolution is an efficient, effective and robust evolutionary optimization method. Still, DE takes large computational time for optimizing the computationally expensive objective functions. And therefore, an attempt to speed up DE is considered necessary. This paper introduces a modification to original DE that enhances the convergence rate without compromising on solution quality. Our modified differential evolution (MDE) algorithm utilizes only one set of population as against two sets in original DE at any given point of time in a generation. Such an improvement reduces the memory and computational efforts. The proposed MDE is applied to benchmark test functions and five non-linear chemical engineering problems. Results obtained are compared with those obtained using DE by considering the convergence history (CPU time and the number of runs converged to global optimum) and the established statistical techniques, taking into account the variability in the results, such as t-test. As compared to DE, MDE is found to perform better in locating the global optimal solution for all the problems considered.

Experimental studies on producer gas generation from wood waste in a downdraft biomass gasifier.

A process of conversion of solid carbonaceous fuel into combustible gas by partial combustion is known as gasification. The resulting gas, known as producer gas, is more versatile in its use than the original solid biomass. In the present study, a downdraft biomass gasifier is used to carry out the gasification experiments with the waste generated while making furniture in the carpentry section of the institutes workshop. Dalbergia sisoo, generally known as sesame wood or rose wood is mainly used in the furniture and wastage of the same is used as a biomass material in the present gasification studies. The effects of air flow rate and moisture content on biomass consumption rate and quality of the producer gas generated are studied by performing experiments. The performance of the biomass gasifier system is evaluated in terms of equivalence ratio, producer gas composition, calorific value of the producer gas, gas production rate, zone temperatures and cold gas efficiency. Material balance is carried out to examine the reliability of the results generated. The experimental results are compared with those reported in the literature.

Differential evolution for multi-objective optimization

Two test problems on multiobjective optimization (one simple general problem and the second one on an engineering application of cantilever design problem) are solved using differential evolution (DE). DE is a population based search algorithm, which is an improved version of genetic algorithm (GA), Simulations carried out involved solving (1) both the problems using Penalty function method, and (2) first problem using Weighing factor method and finding Pareto optimum set for the chosen problem, DE found to be robust and faster in optimization. To consolidate the power of DE, the classical Himmelblau function, with bounds on variables, is also solved using both DE and GA. DE found to give the exact optimum value within less generations compared to simple GA.

Estimation of heat transfer parameters in a trickle-bed reactor using differential evolution and orthogonal collocation

A new non-sequential technique is proposed for the estimation of effective heat transfer parameters using radial temperature profile measurements in a gas–liquid co-current downflow through packed bed reactors (often referred to as trickle bed reactors). Orthogonal collocation method combined with a new optimization technique, differential evolution (DE) is employed for estimation. DE is an exceptionally simple, fast and robust, population based search algorithm that is able to locate near-optimal solutions to difficult problems. The results obtained from this new technique are compared with that of radial temperature profile (RTP) method. Results indicate that orthogonal collocation augmented with DE offer a powerful alternative to other methods reported in the literature. The proposed technique takes less computational time to converge when compared to the existing techniques without compromising with the accuracy of the parameter estimates. This new technique takes on an average 10 s on a 90 MHz Pentium processor as compared to 30 s by the RTP method. This new technique also assures of convergence from any starting point and requires less number of function evaluations.

Modeling for pyrolysis of solid particle: kinetics and heat transfer effects

Abstract In the present study, a mathematical model to describe the pyrolysis of a single solid particle of biomass is developed by incorporating improvements in the existing model reported in literature. It couples the heat transfer equation with the chemical kinetics equations. The pyrolysis rate has been simulated by a kinetic scheme involving three reactions (primary and secondary): two parallel reactions and a third for the secondary interactions between the volatile and gaseous products and the char. The dependence of convective heat transfer coefficient on Reynolds number and Prandtl number is incorporated in the model. A finite difference method using a pure implicit scheme is used for solving the heat transfer equation and the Runge–Kutta 4th order method for the chemical kinetics equations. The model equation is solved for cylindrical pellets, spheres and slab geometries of equivalent radius ranging from 0.00025 to 0.013 m and temperature ranging from 303 to 1000 K. The simulated results obtained using the present model are in excellent agreement with the experimental data, much better than the agreement with the earlier models reported in the literature.

Utilization of waste product (tamarind seeds) for the removal of Cr(VI) from aqueous solutions: equilibrium, kinetics, and regeneration studies.

In the present study, an adsorbent was prepared from tamarind seeds and used after activation for the removal of Cr(VI) from aqueous solutions. The tamarind seeds were activated by treating them with concentrated sulfuric acid (98% w/w) at a temperature of 150 degrees C. The adsorption of Cr(VI) was found to be maximum at low values of initial pH in the range of 1-3. The adsorption process of Cr(VI) was tested with Langmuir, Freundlich, Redlich-Peterson, Koble-Corrigan, Tempkin, Dubinin-Radushkevich and Generalized isotherm models. Application of the Langmuir isotherm to the system yielded a maximum adsorption capacity of 29.7 mg/g at an equilibrium pH value ranging from 1.12 to 1.46. The adsorption process followed second-order kinetics and the corresponding rate constants obtained were 2.605 x 10(-3), 0.818 x 10(-3), 0.557 x 10(-3) and 0.811 x 10(-3) g/mg min(-1) for 50, 200, 300 and 400 mg/L of initial Cr(VI) concentration, respectively. The regenerated activated tamarind seeds showed more than 95% Cr(VI) removal of that obtained using the fresh activated tamarind seeds. A feasible solution is proposed for the disposal of the contaminants (acid and base solutions) containing high concentrations of Cr(VI) obtained during the regeneration (desorption) process.

Modeling, simulation and estimation of optimum parameters in pyrolysis of biomass

Abstract Pyrolysis is a process by which a biomass feedstock is thermally degraded in the absence of air/oxygen. It is used for the production of solid (charcoal), liquid (tar and other organics) and gaseous products. The present work involves the estimation of optimum parameters in the pyrolysis of biomass for both non-isothermal and isothermal conditions. The modeling equations are solved numerically using the fourth order Runge–Kutta method over a wide range of heating rates (25–360 K/s) and temperatures (773–1773 K). The simulated results are compared with those reported in the literature and found to be in good agreement qualitatively in the range of operating conditions covered, but some very interesting trends are found, especially with respect to the effect of net heating rate and temperature on final pyrolysis time. The final pyrolysis time first decreases at lower values of net heating rate or temperature and then increases as net heating rate or temperature is further increased, providing an optimum value of net heating rate or temperature at which final pyrolysis time is minimum. This interesting phenomenon, which was not reported by investigators earlier, is well explained by means of the pyrolysis kinetics.

Modeling, simulation, and experimental validation for continuous Cr(VI) removal from aqueous solutions using sawdust as an adsorbent

Continuous adsorption experiments were performed in a fixed-bed adsorption column to evaluate the performance of low-cost adsorbent (sawdust) developed for the removal of Cr(VI) from aqueous solutions. The effects of influencing parameters such as flow rate, mass of adsorbent, initial Cr(VI) concentration were studied and the corresponding breakthrough curves were obtained. The fixed-bed adsorption process parameters such as breakthrough time, total percentage removal of Cr(VI), adsorption exhaustion rate and fraction of unused bed-length were obtained. A mathematical model for fixed-bed adsorption column was proposed by incorporating the effect of velocity variation along the bed-length in the existing model. Pore and solid diffusion models were used to describe the intra-particle mechanism for Cr(VI) adsorption. The proposed mathematical model was validated with the literature data and the experimental data obtained in the present study and the model was found to be good for explaining the behavior of breakthrough curves.

Parametric study of thermal and thermodynamic properties on pyrolysis of biomass in thermally thick regime

Abstract In the present study, a simultaneous chemical kinetics and heat transfer model is used to predict the effects of the most important thermal and thermodynamic properties (thermal conductivity, heat transfer coefficient, emissivity and heat of reaction number) of the feedstock on the convective-radiant pyrolysis of biomass fuels. A finite difference pure implicit scheme utilizing the tri-diagonal matrix algorithm is employed for solving the heat transfer model equation. The Runge–Kutta fourth-order method is used for the chemical kinetics model equations. Simulations are performed considering cylindrical pellets of equivalent radius ranging from 0.003 to 0.011 m and temperatures ranging from 303 to 900 K. For conversion in the thermally thick regime (intra-particle heat transfer control), it is found that variations in the properties mainly affect the activity of the primary reactions. Sensitivity analysis is conducted to find the most dominant properties affecting the pyrolysis and found that the highest sensitivity is associated with the emissivity and thermal conductivity of the biomass. Applications of these findings in reactor design and operation are discussed. The results obtained using the improved models are in excellent agreement with the experimental data, much better than the agreement with the earlier models reported in the literature.