Citrus peel influences the production of an extracellular naringinase by Staphylococcus xylosus MAK2 in a stirred tank reactor

Staphylococcus xylosus MAK2, Gram-positive coccus, a nonpathogenic member of the coagulase-negative Staphylococcus family was isolated from soil and used to produce naringinase in a stirred tank reactor. An initial medium at pH 5.5 and a cultivation temperature of 30°C was found to be optimal for enzyme production. The addition of Ca⁺² caused stimulation of enzyme activity. The effect of various physico-chemical parameters, such as pH, temperature, agitation, and inducer concentration was studied. The enzyme production was enhanced by the addition of citrus peel powder (CPP) in the optimized medium. A twofold increase in naringinase production was achieved using different technological combinations. The process optimization using technological combinations allowed rapid optimization of large number of variables, which significantly improved enzyme production in a 5-l reactor in 34 h. An increase in sugar concentration (15 gl^-1) in the fermentation medium further increased naringinase production (8.9 IUml^-1) in the bioreactor. Thus, availability of naringinase renders it attractive for potential biotechnological applications in citrus processing industry.

Studying non-uniform electrodeposition using the wire beam electrode method

Nonuniform electrodeposition is a major concern in almost every practical electrodeposition application. The ablility to control nonuniformity in electrodeposition is the key to successful plating for corrosion resistance, and more especially to meeting the very exacting requirements of electroforming, electrodeposition. This paper presents a novel technique namely the wire beam electrode (WBE) for characterizing and monitoring nonuniform electroplating processes. For the first time, the nonuniform distribution of electroplating currents (NDEC) has been mapped. Preliminary experimental results indicated that electrochemical heterogeneity was the key factor affecting NDEC. The secondary current distribution, rather than the primary current distribution, played a major role in determining the NDEC. This work suggests that the WBE is a practical tool for characterizing and optimizing electrodeposition processes and for verifying the accuracy and completeness of mathematic modelling of electrodeposition processes.

Artificial neural network modeling of flow stress in hot rolling

In this study, an artificial neural network model is proposed to predict the flow stress variations during the hot rolling process. Optimization of the proposed neural network with respect to number of neurons within the hidden layer, different training methods and transfer functions of the neural network is performed. The results of the optimal network were compared with those of the conventional analytic method and it is shown that using an optimal neural network the mean calculated error is drastically reduced.

Simulation optimization for process scheduling through simulated annealing

This paper presents a simulation optimization of a real scheduling problem in industry, simulated annealing is introduced for this purpose.  Investigation is performed into the practicality of using simulated annealing to produce high quality schedules.  Results on the solution quality and computational effort show the inherent properties of the simulated annealing.  It is shown that when using this method, high quality schedules can be produced within reasonable time contraints.

Optimization of process parameters for the production of naringinase by Aspergillus niger MTCC 1344

Aspergillus niger MTCC 1344 was used to produce extracellular naringinase in a complex (molasses, yeast extract and salts) medium. An initial medium pH 4.5 and cultivation temperature 30 °C were optimal for enzyme production. Among various carbon and organic nitrogen sources used, molasses and peptone were the most effective for enzyme yield. The rate of enzyme production was enhanced when metal ions were added to the medium. Fermentation conditions are described which produced a higher rate of enzyme synthesis. An increase in initial sugar concentration from 6 to 10 g l⁻¹ in the fermentation medium produced decreased naringinase synthesis while cell mass growth increased with the increase of sugar concentration. At a higher sugar level (10 g l⁻¹) the production of cell mass decreased.

Optimization of medium and process parameters for the production of inulinase from a newly isolated Kluyveromyces marxianus YS-1

A newly isolated strain of Kluyveromyces marxianus YS-1 was used for the production of extra cellular inulinase in a medium containing inulin, meat extract, CaCl₂ and sodium dodecyl sulphate (SDS). Fermentation medium pH 6.5, cultivation temperature 30 °C and 5% (v/v) inoculum of 12 h-old culture were optimal for enzyme production (30.8 IU/ml) with a fermentation time of 72 h at shake flask level. Raw inulin (2%, w/v) extracted from dahlia tubers by processing at 15 kg/cm² for 10 min was optimum for bioreactor studies. Maximum enzyme production (55.4 IU/ml) was obtained at an agitation rate of 200 rpm and aeration of 0.75 vvm in a stirred tank reactor with a fermentation time of 60 h.

Stochastic optimization models for energy management in carbonization process of carbon fiber production

Industrial producers face the task of optimizing production process in an attempt to achieve the desired quality such as mechanical properties with the lowest energy consumption. In industrial carbon fiber production, the fibers are processed in bundles containing (batches) several thousand filaments and consequently the energy optimization will be a stochastic process as it involves uncertainty, imprecision or randomness. This paper presents a stochastic optimization model to reduce energy consumption a given range of desired mechanical properties. Several processing condition sets are developed and for each set of conditions, 50 samples of fiber are analyzed for their tensile strength and modulus. The energy consumption during production of the samples is carefully monitored on the processing equipment. Then, five standard distribution functions are examined to determine those which can best describe the distribution of mechanical properties of filaments. To verify the distribution goodness of fit and correlation statistics, the Kolmogorov-Smirnov test is used. In order to estimate the selected distribution (Weibull) parameters, the maximum likelihood, least square and genetic algorithm methods are compared. An array of factors including the sample size, the confidence level, and relative error of estimated parameters are used for evaluating the tensile strength and modulus properties. The energy consumption and N2 gas cost are modeled by Convex Hull method. Finally, in order to optimize the carbon fiber production quality and its energy consumption and total cost, mixed integer linear programming is utilized. The results show that using the stochastic optimization models, we are able to predict the production quality in a given range and minimize the energy consumption of its industrial process.

Mathematical modelling of the refractive index reconstruction through global optimization

We examine a mathematical model of non-destructive testing of planar waveguides, based on numerical solution of a nonlinear integral equation. Such problem is ill-posed, and the method of Tikhonov regularization is applied. To minimize Tikhonov functional, and find the parameters of the waveguide, we use two new optimization methods: the cutting angle method of global optimization, and the discrete gradient method of nonsmooth local optimization. We examine how the noise in the experimental data influences the solution, and how the regularization parameter has to be chosen. We show that even with significant noise in the data, the numerical solution is of high accuracy, and the method can be used to process real experimental da.ta..

Benchmark generation algorithm for stochastic mixed model assembly shop simulation and optimization

The Operations Research (OR) community have defined many deterministic manufacturing control problems mainly focused on scheduling. Well-defined benchmark problems provide a mechanism for communication of the effectiveness of different optimization algorithms. Manufacturing problems within industry are stochastic and complex. Common features of these problems include: variable demand, machine part specific breakdown patterns, part machine specific process durations, continuous production, Finished Goods Inventory (FGI) buffers, bottleneck machines and limited production capacity. Discrete Event Simulation (DES) is a commonly used tool for studying manufacturing systems of realistic complexity. There are few reports of detail-rich benchmark problems for use within the simulation optimization community that are as complex as those faced by production managers. This work details an algorithm that can be used to create single and multistage production control problems. The reported software implementation of the algorithm generates text files in eXtensible Markup Language (XML) format that are easily edited and understood as well as being cross-platform compatible. The distribution and acceptance of benchmark problems generated with the algorithm would enable researchers working on simulation and optimization of manufacturing problems to effectively communicate results to benefit the field in general.

Optimization of MLP parameters on mineral potential mapping tasks

Mineral potential mapping is the process of combining a set of input maps, each representing a distinct geo-scientific variable, to produce a single map which ranks areas according to their potential to host deposits of a particular type. The maps are combined using a mapping function which must be either provided by an expert (knowledge-driven approach), or induced from sample data (data-driven approach). Current data-driven approaches using multilayer perceptrons (MLPs) to represent the mapping function have several inherent problems: they rely heavily on subjective judgment in selecting training data and are highly sensitive to this selection; they do not utilize the contextual information provided by unlabeled data; and, there is no objective interpretation of the values output by the MLP. This paper presents a novel approach which overcomes these three problems.

Evolutionary multiobjective optimization in engineering management: an empirical study on bridge deck rehabilitation

There exist multiple objectives in engineering management such as minimum cost and maximum service capacity. Although solution methods of multiobjective optimization problems have undergone continual development over the past several decades, the methods available to date are not particularly robust, and none of them performs well on the broad classes. Because genetic algorithms work with a population of points, they can capture a number of solutions simultaneously, and easily incorporate the concept of Pareto optimal set in their optimization process. In this paper, a genetic algorithm is modified to deal with the rehabilitation planning of bridge decks at a network level by minimizing the rehabilitation cost and deterioration degree simultaneously.

Evolutionary multiobjective optimization in engineering management: an empirical application in infrastructure systems

Generally multiple objectives exist in transportation infrastructure management, such as minimum cost and maximum service capacity. Although solution methoak of multiobjective optimization problems have undergone continual development over the part several decades, the methods available to date are not particularly robust, and none of them perform well on the broad classes. Because genetic algorithms work with apopulation ofpoints, they can capture a number of solutions simultaneously, and easily incorporate the concept of a Pareto optimal set in their optimization process. In this paper, a genetic algorithm is modified to deal with an empirical application for the rehabilitation planning of bridge decks, at a network level, by minimizing the rehabilitation cost and deterioration degree simultaneously.

Integrated optimization system for high pressure die casting processes

High pressure die casting (HPDC) is a versatile process for producing engineered metal parts by forcing molten metal under high pressure into reusable steel dies. However there are a large number of attributes involved which contribute to the complexity of the process. A novel integrated approach is developed to optimize the high pressure die casting processes. The die temperature profiles will be studied with infrared thermograph technology and the internal cooling system will be optimized to provide even cooling to the components and the die. The heat stored in the die and the components is studied with image processing. Based on the geometrical profile of the components, cooling channels can be redesigned to improve the cooling efficiency while the cooling time is reduced. This will not only significantly improve the quality of the castings but also improve the productivity of the process.