分段控温黄原胶发酵过程

温度是影响黄原胶发酵的重要因素之一。为了优化黄原胶发酵过程的温度控制,在5 L通气式搅拌发酵罐中,非溶氧限制的条件下,对恒温黄原胶发酵过程进行了研究。结果显示,发酵温度为28℃时,在发酵稳定期有较高的菌体浓度,所得黄原胶的丙酮酸含量与表观粘度较高,但有较长的发酵周期。而发酵温度为33℃时,有较短的发酵周期,在发酵稳定期有较高的比产胶速率,但所得黄原胶的丙酮酸含量与表观粘度较低。实验测定了控制黄原胶发酵生长期温度为28℃,稳定期温度为33℃的发酵过程数据。结果表明,与恒温发酵过程相比,通过温度的分段控制可以缩短发酵周期,提高产胶浓度及改善胶的品质,分段控温是黄原胶发酵的一种方便有效的调节手段。     

Comparison of Lactic Acid Productivities at High Substrate Conversions in a Continuous Two-stage Fermenter With Cell Recycle Using Different Kinetic Models

The steady-state performance of a two-stage recycle fermenter with two different types (I and II) of configurations for staging was investigated numerically for the continuous production of lactic acid. In Type I the bleed broth withdrawn from the first stage is supplied to the second fermenter, and in Type II both filtrate and bleed broth from the first stage are introduced to the second stage. Using four different kinetic models taken from the literature, the effects of operating parameters on the overall lactic acid productivities and the overall substrate conversions are examined. At moderate conversions, productivities for Type I operation are found to be higher than those for Type II and for the single-stage system. In the case of high conversions close to complete consumption, Type II operation is more efficient than the Type I and single-stage systems. For 99% conversion and 40 kg m^-3 substrate feed concentration, productivities for Type II are predicted to be 14-74% higher compared to those for the single-stage system at the same bleed ratios.     

INFLUENCE OF DESIGN AND OPERATING CONDITIONS ON THE SYSTEM PERFORMANCE OF A TWO-STAGE ADSORPTION CHILLER

This article aims at clarifying the possible design and operating conditions for silica gel-water adsorption refrigeration cycles driven by near-ambient temperature waste heat sources (between 45 and 75°C) with relatively small regenerating temperature lifts (15 to 45 K). A two-stage silica gel-water advanced adsorption chiller is introduced and a simulation model of the chiller was developed to analyze the influence of operating and design conditions on the system performance (coefficient of performance, COP, and cooling capacity). It was hypothesized that the proposed chiller can be driven by low temperature waste heat at 55°C to produce effective cooling. Simulation results show that the operating conditions such as cycle time and hot and cooling water inlet temperature have an influential effect on cooling capacity and COP. COP is proportional to cycle time and heat transfer coefficient as well as inversely proportional to the cooling water inlet temperature, while there are optimum values of hot water temperature and silica gel weight for maximum COP. Cooling capacity mainly improves with the addition of silica gel weight and decreases as cooling water temperature increases. Simulation results also revealed that the system performance can be improved significantly by setting the design and operating conditions optimally.     

Two-stage stochastic minimum cost consensus models with asymmetric adjustment costs

When dealing with consensus cost problems with asymmetric adjustment costs, the uncertain scenarios with certain probabilities which are becoming a serious problem decision-makers have to face. However, existing optimization-based consensus models have failed to consider uncertain factors that could influence the final consensus and total consensus cost. In order to better deal with these issues, it is necessary to develop practical consensus optimal models. Thus, we establish three two-stage stochastic minimum cost consensus models with asymmetric adjustment costs that may eventually lead the way to better consensus outcomes. The impact of uncertain parameters (such as individual opinions, unit asymmetric adjustment costs, compromise limits, cost-free thresholds) are investigated by modeling three kinds of uncertain consensus models. We solve the proposed two-stage stochastic consensus problem iteratively using the L-shaped algorithm and show the convergence of the algorithm. Furthermore, a case of pollution control negotiations verifies the practicability of the proposed models. Moreover, the comparison of results with the L-shaped algorithm and CPLEX shows that the L-shaped algorithm is more effective in solving time. Some discussions and comparisons on local and global sensitivity analysis of the uncertain parameters are presented to reveal the features of the proposed models. Finally, the relationships between the minimum cost consensus model and minimum cost consensus models with asymmetric adjustment costs and the proposed models are also provided.     

Asynchronous action-reward learning for nonstationary serial supply chain inventory control

Action-reward learning is a reinforcement learning method. In this machine learning approach, an agent interacts with non-deterministic control domain. The agent selects actions at decision epochs and the control domain gives rise to rewards with which the performance measures of the actions are updated. The objective of the agent is to select the future best actions based on the updated performance measures. In this paper, we develop an asynchronous action-reward learning model which updates the performance measures of actions faster than conventional action-reward learning. This learning model is suitable to apply to nonstationary control domain where the rewards for actions vary over time. Based on the asynchronous action-reward learning, two situation reactive inventory control models (centralized and decentralized models) are proposed for a two-stage serial supply chain with nonstationary customer demand. A simulation based experiment was performed to evaluate the performance of the proposed two models. Chang Ouk Kim received his Ph.D. in industrial engineering from Purdue University in 1996 and his B.S. and M.S. degrees from Korea University, Republic of Korea in 1988 and 1990, respectively. From 1998--2001, he was an assistant professor in the Department of Industrial Systems Engineering at Myongji University, Republic of Korea. In 2002, he joined the Department of Information and Industrial Engineering at Yonsei University, Republic of Korea and is now an associate professor. He has published more than 30 articles at international journals. He is currently working on applications of artificial intelligence and adaptive control theory in supply chain management, RFID based logistics information system design, and advanced process control in semiconductor manufacturing. Ick-Hyun Kwon is a postdoctoral researcher in the Department of Civil and Environmental Engineering at University of Illinois at Urbana-Champaign. Previous to this position, Dr. Kwon was a research assistant professor in the Research Institute for Information and Communication Technology at Korea University, Seoul, Republic of Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, in 1998, 2000, and 2006, respectively. His current research interests are supply chain management, inventory control, production planning and scheduling. Jun-Geol Baek is an assistant professor in the Department of Business Administration at Kwangwoon University, Seoul, Korea. He received his B.S., M.S., and Ph.D. degrees in Industrial Engineering from Korea University, Seoul, Korea, in 1993, 1995, and 2001 respectively. From March 2002 to February 2007, he was an assistant professor in the Department of Industrial Systems Engineering at Induk Institute of Technology, Seoul, Korea. His research interests include machine learning, data mining, intelligent machine diagnosis, and ubiquitous logistics information systems. An erratum to this article can be found at     

Outsourcing strategy in two-stage call centers

This paper considers ‘two-stage’ call centers where some incoming calls are completed by first service while others require an additional second service. Although this type of call center is not uncommon, it has not been dealt with, if any, in the call center literature. In this paper, we introduce the concept of the ‘two-stage’ call center and discuss its features. Furthermore, we develop an effective outsourcing strategy in ‘two-stage’ call centers. To this end, we model ‘two-stage’ service system and propose several call routing structures. The structures are compared through numerical test and conventional queueing theories form the theoretical basis of our study.     

Simulation-based two-stage estimation for multiple nonparametric regression

To reduce the curse of dimensionality arising from nonparametric estimation procedures for multiple nonparametric regression, in this paper we suggest a simulation-based two-stage estimation. We first introduce a simulation-based method to decompose the multiple nonparametric regression into two parts. The first part can be estimated with the parametric convergence rate and the second part is small enough so that it can be approximated by orthogonal basis functions with a small trade-off parameter. Then the linear combination of the first and second step estimators results in a two-stage estimator for the multiple regression function. Our method does not need any specified structural assumption on the regression function and it is proved that the newly proposed estimation is always consistent even if the trade-off parameter is designed to be small. Thus when the common nonparametric estimator such as local linear smoothing collapses because of the curse of dimensionality, our estimator still works well.     

Arabic handwritten digit recognition

In this paper, we fill a gap in the literature by studying the problem of Arabic handwritten digit recognition. The performances of different classification and feature extraction techniques on recognizing Arabic digits are going to be reported to serve as a benchmark for future work on the problem. The performance of well known classifiers and feature extraction techniques will be reported in addition to a novel feature extraction technique we present in this paper that gives a high accuracy and competes with the state-of-the-art techniques. A total of 54 different classifier/features combinations will be evaluated on Arabic digits in terms of accuracy and classification time. The results are analyzed and the problem of the digit ‘0’ is identified with a proposed method to solve it. Moreover, we propose a strategy to select and design an optimal two-stage system out of our study and, hence, we suggest a fast two-stage classification system for Arabic digits which achieves as high accuracy as the highest classifier/features combination but with much less recognition time.     

Relaxed parallel two-stage multisplitting methods II: Asynchronous version

Two classes of asynchronous relaxed parallel two-stage multisplitting methods based on extrapolated and AOR methods are studied for the solution of nonsingular linear systems, which are called asynchronous outer relaxed or inner relaxed parallel two-stage multisplitting methods. Convergence of these methods is studied for H-matrix. Almost all methods seen in literatures can be viewed as special cases of our methods.     

A genetic algorithm for solving the fixed-charge transportation model: Two-stage problem

Transportation of goods in a supply chain from plants to customers through distribution centers (DCs) is modeled as a two-stage distribution problem in the literature. In this paper we propose genetic algorithms to solve a two-stage transportation problem with two different scenarios. The first scenario considers the per-unit transportation cost and the fixed cost associated with a route, coupled with unlimited capacity at every DC. The second scenario considers the opening cost of a distribution center, per-unit transportation cost from a given plant to a given DC and the per-unit transportation cost from the DC to a customer. Subsequently, an attempt is made to represent the two-stage fixed-charge transportation problem (Scenario-1) as a single-stage fixed-charge transportation problem and solve the resulting problem using our genetic algorithm. Many benchmark problem instances are solved using the proposed genetic algorithms and performances of these algorithms are compared with the respective best existing algorithms for the two scenarios. The results from computational experiments show that the proposed algorithms yield better solutions than the respective best existing algorithms for the two scenarios under consideration.