Taguchi-factorial type-2 fuzzy random optimization model for planning conjunctive water management with compound uncertainties

In this study, a type-2 fuzzy random optimization (TFRO) method is developed for planning conjunctive water management system associated with compound uncertainties. TFRO can effectively address compound uncertainties expressed as type-2 fuzzy sets, probability distributions, and type-2 fuzzy random variables. Solution algorithm based on the degree of probability and the information of plausibility is proposed to transform nonlinear objective function and constraints into their linear equivalents. A real case of water-resources allocation problem in Zhangweinan River Basin (China) is employed to demonstrate the applicability of the proposed method. A Taguchi-factorial type-2 fuzzy random model is also formulated through introducing Taguchi design and ANOVA technique into the TFRO framework. Results obtained can help reveal the relationship among multiple impact factors of economic, environmental and resource (water conveyance efficiency, water delivery cost, and system violation risk), as well as quantify their contributions to the variability of system benefit and water allocation schemes.     

Disjoint convex shell and its applications in mesh unfolding

In this work, we study a geometric structure called disjoint convex shell or simply DC-shell. A DC-shell of a polyhedron is a set of pairwise interior disjoint convex objects that collectively approximate the given polyhedron. Preventing convex objects from overlapping enables faster and robust collision response and more realistic fracturing simulation. Without the disjointness constraint, a physical realization of the approximation becomes impossible. This paper investigates multiple approaches that construct DC-shells from shapes that are either composed of overlapping components or segmented into parts. We show theoretically that, even under this rather simplified setting, constructing DC-shell is difficult.To demonstrate the power of DC-shell, we studied how DC-shell can be used in mesh unfolding, an important computational method in manufacturing 3D shape from the 2D material. Approximating a given polyhedron model by DC-shells provides two major benefits. First, they are much easier to unfold using the existing unfolding methods. Second, they can be folded easily by both human folder or self-folding machines. Consequently, DC-shell makes paper craft creation and design more accessible to younger children and provides chances to enrich their education experiences.     

Mechanism optimization based on reaction rate rules

Accurate chemistry models form the backbone of detailed computational fluid dynamics (CFD) tools used for simulating complex combustion devices. Combustion chemistry is often very complex and chemical mechanisms generally involve more than one hundred species and one thousand reactions. In the derivation of these large chemical mechanisms, typically a large number of reactions appears, for which rate data are not available from experiment or theory. Rate data for these reactions are then often assigned using so-called reaction classes. This method categorizes all possible fuel-specific reactions as classes of reactions with prescribed rules for the rate constants. This ensures consistency in the chemical mechanism. In rate parameter optimizations found in the published literature, rate constants of single elementary reactions are usually systematically optimized to achieve good agreement between model performance and experimental measurements. However, it is not kinetically reasonable to modify the rate parameters of single reactions, because this will violate consistency of rate parameters of kinetically similar reactions. In this work, the rate rules, that determine the rates for reaction classes are calibrated instead of the rates of single elementary reactions leading to a chemically more consistent model optimization. This is demonstrated by optimizing an n-pentane combustion mechanism. The rate rules are studied with respect to reaction classes, abstracting species, broken C–H bonds, and ring strain energy barriers. Furthermore, the uncertainties of the rate rules and model predictions are minimized and the pressure dependence of reaction classes dominating low temperature oxidation is optimized.     

Process simulation using the expanded fluid model for viscosity calculations

The expanded fluid (EF) viscosity model was implemented and further developed for efficient integration into a commercial process simulator (VMGSim™). The model has three adjustable parameters per component and its inputs are density, pressure and low pressure gas viscosity. The model was adapted to use densities determined by the Rackett correlation (liquid phase) and the Advanced Peng–Robinson Equation of State (vapor phase). The enhanced EF model fit experimental viscosities of pure hydrocarbons, water and polar compounds important for the simulation of oil and natural gas systems with average absolute errors just above 5%. The implemented EF model was tested against experimental viscosity data that included hydrocarbon and aqueous mixtures with average absolutes errors of 0.7 and 6.2% respectively. Generalized expressions for the estimation interaction parameters of binary mixtures involving paraffins, naphthenes, aromatics, alcohols, glycols and water were obtained. The EF model was also applied to crude oil (bitumen) examples. The three key developments for the efficient implementation of the EF model in a commercial simulator were: (1) the appropriate selection of phase density models; (2) the automatic determination of model fluid specific parameters; and (3) the use of generalized mixing rules for the calculation of binary interaction parameters.     

Decentralized and centralized model predictive control to reduce the bullwhip effect in supply chain management

Mitigating the bullwhip effect is one of crucial problems in supply chain management. In this research, centralized and decentralized model predictive control strategies are applied to control inventory positions and to reduce the bullwhip effect in a benchmark four-echelon supply chain. The supply chain under consideration is described by discrete dynamic models characterized by balance equations on product and information flows with an ordering policy serving as the control schemes. In the decentralized control strategy, a MPC-EPSAC (Extended Prediction Self-Adaptive Control) approach is used to predict the changes in the inventory position levels. A closed-form solution of an optimal ordering decision for each echelon is obtained by locally minimizing a cost function, which consists of the errors between predicted inventory position levels and their setpoints, and a weighting function that penalizes orders. The single model predictive controller used in centralized control strategy optimizes globally and finds an optimal ordering policy for each echelon. The controller relies on a linear discrete-time state-space model to predict system outputs. But the predictions are approached by either of two multi-step predictors depending on whether the states of the controller model are directly observed or not. The objective function takes a quadratic form and thus the resulting optimization problem can be solved via standard quadratic programming method. The comparisons on performances of the two MPC strategies are illustrated with a numerical example.     

Enhancement on sensitivity analysis of priority in analytic hierarchy process

The sensitivity analysis of priority in AHP was initiated by Masuda (1990). Although the Masuda sensitivity coefficient is by far one of the best, this note will point out some of its disadvantages and suggest an alternative sensitivity coefficient. The same example used in Masuda (1990) is discussed here for comparison and this shows clearly that our new coefficients reveal the sensitivity of an AHP more accurately than Masuda's coefficients.     

层次分析法在铁路T梁架设方案比选中的应用

为从贵广铁路DK775+300~DK803+700段T梁架设工程备选方案中比选获得最优方案,采用层次分析法辅助进行决策分析,经求解得出了组合权重,确定了最优方案,该方案经实践检验正确,满足了建设要求,值得推广应用。     

A process synthesis-intensification framework for the development of sustainable membrane-based operations

In this paper a multi-level, multi-scale framework for process synthesis-intensification that aims to make the process more sustainable than a base-case, which may represent a new process or an existing process, is presented. At the first level (operation-scale) a conceptual base case design is synthesized through the sequencing of unit operations and subsequently analyzed for identifying process hot-spots using economic, life cycle and sustainability metrics. These hot-spots are limitations/bottlenecks associated with tasks that may be targeted for overall process improvement. At the second level (task-scale) a task-based synthesis method is applied where one or more tasks representing unit operations are identified and analyzed in terms of means-ends for generating intensified flowsheet alternatives. At the third level (phenomena-scale) a phenomena-based synthesis method is applied, where the involved phenomena in various tasks are identified, manipulated and recombined to generate new and/or existing unit operations configured into flowsheet alternatives that target the tasks associated with hot-spots. Every lower-scale or higher-level, generates more alternatives than their corresponding larger-scale. Those alternatives that are able to address the identified hot-spots therefore give innovative and more sustainable process designs that otherwise could not be found from the larger-scales. In this paper, membrane-based operations identified through this framework are highlighted in terms of extension of the combined intensification-synthesis method and its application to generate membrane-based operations. Also, application of the framework is illustrated through a case study involving the production of methyl acetate where membrane-based intensified operations play a major role in determining more sustainable process design alternatives.     

Multi-response optimization of post-fire performance of strain hardening cementitious composite

This paper presents results of an experimental program conducted to optimize the post-fire performance of Strain Hardening Cementititous Composites (SHCC) using Taguchi approach with utility concept. The experiments were first undertaken by determining nine SHCC mixes using a standard L9 (3⁴) orthogonal array of four parameters and each parameter with three levels. The four parameters of SHCC mixes included fly-ash/binder ratio, sand/binder ratio, water/binder ratio and fiber proportions. The responses of SHCC to be optimized were tensile strain capacity, compressive strength and post-fire compressive strength after subjected to 200 °C, 400 °C, 600 °C and 800 °C of isothermal heating. Utility concept was introduced to simplify the multi-response problem into mono-response question together with Taguchi method. The role of different parameters on the composite responses of SHCC was examined. Furthermore, an optimal SHCC mix to maximize multi-responses was determined based on statistical analysis and validated by additional confirmation tests.     

Integrated cost optimization in a two-stage,automotive supply chain

The efficiency of the automotive supply chain is crucial for ensuring the competitiveness of the automotive industry, which represents one of the most significant manufacturing sectors. We model the integrated production and transportation planning problem of a Tier-1 automotive supplier while taking into account realistic conditions such as sequence-dependent setups on multiple injection molding machines operating in parallel, auxiliary resource assignments of overhead cranes, and multiple types of costs. Finished parts go to the integrated supply chain׳s second stage, transportation, for subsequent delivery by capacitated vehicles to multiple distribution centers for meeting predefined due date requirements. We develop a mixed-integer, linear programming model of the problem, and then present a hybrid simulated annealing algorithm (HSAA), including a constructive heuristic. Our proposed HSAA employs an effective encoding-decoding strategy to solve the NP-hard problem to near optimality in a timely manner. Computational results demonstrate the promising performance of the proposed solution approach.