Stochastic modeling and multi-objective optimization for the APECS system

The Advanced Process Engineering Co-Simulator (APECS), developed at the U.S. Department of Energy's (DOE) National Energy Technology Laboratory, is an integrated software suite that enables the process and energy industries to optimize overall plant performance with respect to complex thermal and fluid flow phenomena. The APECS system uses the process-industry standard CAPE-OPEN (CO) interfaces to combine equipment models and commercial process simulation software with powerful analysis and virtual engineering tools. The focus of this paper is the CO-compliant stochastic modeling and multi-objective optimization capabilities provided in the APECS system for process optimization under uncertainty and multiple and sometimes conflicting objectives. The usefulness of these advanced analysis capabilities is illustrated using a simulation and multi-objective optimization of an advanced coal-fired, gasification-based, zero-emissions electricity and hydrogen generation facility with carbon capture.     

Urinary mycoestrogens, body size and breast development in New Jersey girls

Background

Despite extensive research and interest in endocrine disruptors, there are essentially no epidemiologic studies of estrogenic mycotoxins, such as zeranol and zearalenone (ZEA). ZEA mycoestrogens are present in grains and other plant foods through fungal contamination, and in animal products (e.g., meat, eggs, dairy products) through deliberate introduction of zeranol into livestock to enhance meat production, or by indirect contamination of animals through consumption of contaminated feedstuff. Zeranol is banned for use in animal husbandry in the European Union and other countries, but is still widely used in the US. Surprisingly, little is known about the health effects of these mycoestrogens, including their impact on puberty in girls, a period highly sensitive to estrogenic stimulation.

Objectives and methods

We conducted a cross-sectional analysis among 163 girls, aged 9 and 10 years, participating in the Jersey Girl Study to measure urinary mycoestrogens and their possible relationship to body size and development.

Results

We found that mycoestrogens were detectable in urine in 78.5% of the girls, and that urinary levels were predominantly associated with beef and popcorn intake. Furthermore, girls with detectable urinary ZEA mycoestrogen levels tended to be shorter and less likely to have reached the onset of breast development.

Conclusions

Our findings suggest that ZEA mycoestrogens may exert anti-estrogenic effects similar to those reported for isoflavones. To our knowledge, this was the first evaluation of urinary mycoestrogens and their potential health effects in healthy girls. However, our findings need replication in larger studies with more heterogeneous populations, using a longitudinal approach.     

Blood glucose regulation with stochastic optimal control for insulin-dependent diabetic patients

Diabetes is a disease resulting from the impaired mechanism of insulin secretion from the pancreas, which prevents glucose from entering the cells and being utilized and leads to wide swings of blood sugar and many complications such as heart disease and stroke, kidney disease and amputations. In order to prevent these complications and achieve a better quality of life for diabetic patients, effective regulation of blood glucose is essential. This study aims to achieve a better blood glucose control profile by incorporating the time-dependent uncertainties in diabetic patient parameters into formulations of optimal control using a novel approach which originates from finance literature. The time-dependent uncertainties are represented using stochastic processes called Ito processes and the mathematical formulation for this problem is presented. The usefulness of this approach is shown using experimental data from a diabetic patient and stochastic and deterministic optimal control profiles are computed. The stochastic profile results in fewer variations in blood glucose from the reference value of 4.5 mmol/L as compared to the deterministic profile in the presence of parametric uncertainty. This method holds a lot of promise in reducing the wide swings of blood glucose observed in diabetic patients and preventing possible complications of diabetes.     

A metaheuristic approach for optical network optimization problems

Two of the most complex optimization problems encountered in the design of third generation optical networks are the dynamic routing and wavelength assignment (DRWA) problem under the assumptions of ideal and non-ideal physical layers. Both these problems are NP-complete in nature. These are challenging due to the presence of multiple local optima in the search space. Even heuristics-based algorithms fail to solve these problems efficiently as the search space is non-convex. This paper reports the performance of a metaheuristic, that is, an evolutionary programming algorithm in solving different optical network optimization problems. The primary motivation behind adopting this approach is to reduce the algorithm execution time. It is demonstrated that the same basic approach can be used to solve different optimization problems by designing problem-specific fitness functions. Also, it is shown how the algorithm performance can be improved by integrating suitable soft constraints with the original constraints. Exhaustive simulation studies are carried out assuming the presence of different levels of linear impairments such as switch and demultiplexer crosstalk and non-linear impairments like four wave mixing to illustrate the superiority of the proposed algorithms.     

Design of novel polymeric adsorbents for metal ion removal from water using computer-aided molecular design

Heavy metals in drinking water act as contaminants that can cause serious health problems. These metal ions in drinking water are generally removed using cation exchange resins that are used as adsorbents. Generally, chelating resins with limited adsorption capacity are commercially available. Manufacturing novel resin polymers with enhanced adsorption capacity of metal ion requires ample experimental efforts that are expensive as well as time consuming. To overcome these difficulties, application of computer-aided molecular design (CAMD) will be an efficient way to develop novel chelating resin polymers. In this paper, CAMD based on group contribution method (GCM) has been used to design novel resins with enhanced adsorption capability of removing heavy metal ions from water. A polymer consists of multiple monomer units that repeat in a polymer chain. Each repeat unit of the polymer can be subdivided into different structural and functional groups. The adsorption mechanism of heavy metals on resin depends on the difference between activities in adsorbents and the bulk fluid phase. The contribution of the functional groups in the adsorption process is found by estimating the activity coefficient of heavy metal in the solid phase and bulk phase using a modified version of the UNIFAC GCM. The interaction parameters of the functional groups are first determined and then they are used in a combinatorial optimization method for CAMD of novel resin polymers. In this work, designs of novel resin polymers for the removal of Cu ions from drinking water are used as a case study. The proposed new polymer resin has an order of magnitude higher adsorption capacity compared to conventional resin used for the same purpose.     

Synthesis of Silicon Carbide through the Sol—Gel Process from Rayon Fibers

Silicon carbide whiskers (SiC( w )) are synthesized by the pyrolysis of rayon fibers impregnated with sol—gel-derived SiO₂. The influence of boric acid on whisker growth and the effect of varying the TEOS:rayon weight ratio are studied and reported. Evolution of SiC( w ) from impregnated rayon fibers in the carbonized and in the carbothermal stages is investigated.     

Is sustainability achievable? Exploring the limits of sustainability with model systems

Successful implementation of sustainability ideas in ecosystem management requires a basic understanding of the often nonlinear and nonintuitive relationships among different dimensions of sustainability, particularly the system-wide implications of human actions. This basic understanding further includes a sense of the time scale of possible future events and the limits of what is and is not likely to be possible. With this understanding, systematic approaches can then be used to develop policy guidelines for the system. This article presents an illustration of these ideas by analyzing an integrated ecological-economic-social model, which comprises various ecological (natural) and domesticated compartments representing species along with a macroeconomic price setting model. The stable and qualitatively realistic model is used to analyze different relevant scenarios. Apart from highlighting complex relationships within the system, it identifies potentially unsustainable future developments such as increased human per capita consumption rates. Dynamic optimization is then used to develop time-dependent policy guidelines for the unsustainable scenarios using objective functions that aim to minimize fluctuations in the system's Fisher information. The results can help to identify effective policy parameters and highlight the tradeoff between natural and domesticated compartments while managing such integrated systems. The results should also qualitatively guide further investigations in the area of system level studies and policy development.     

Greener by design

Process simulation models and other design tools allow engineers to design, simulate, and optimize chemical processes. However, there is a critical need to incorporate green engineering into the design of these processes. This calls for extending the breadth of the design process. This paper presents an integrated framework for greener design. The framework starts the decision-making as early as the chemical and material selection stage and also includes management and planning decisions. The design goal is not restricted to profitability, but environmental and ecological objectives are also added. However, this integration poses challenging problem of discrete and continuous decisions, nonlinear models, and uncertainties. Furthermore, there are multiple and conflicting objectives to be considered. Therefore, the core of this integrated framework is the efficient algorithmic framework for multiobjective optimization under uncertainty. Two real world case studies are presented that illustrate the promise of such a framework.