Retrofit of heat exchanger networks with pressure recovery of process streams at sub-ambient conditions

This paper presents a new mathematical programming model for the retrofit of heat exchanger networks (HENs), wherein the pressure recovery of process streams is conducted to enhance heat integration. Particularly applied to cryogenic processes, HENs retrofit with combined heat and work integration is mainly aimed at reducing the use of expensive cold services. The proposed multi-stage superstructure allows the increment of the existing heat transfer area, as well as the use of new equipment for both heat exchange and pressure manipulation. The pressure recovery of streams is carried out simultaneously with the HEN design, such that the process conditions (streams pressure and temperature) are variables of optimization. The mathematical model is formulated using generalized disjunctive programming (GDP) and is optimized via mixed-integer nonlinear programming (MINLP), through the minimization of the retrofit total annualized cost, considering the turbine and compressor coupling with a helper motor. Three case studies are performed to assess the accuracy of the developed approach, including a real industrial example related to liquefied natural gas (LNG) production. The results show that the pressure recovery of streams is efficient for energy savings and, consequently, for decreasing the HEN retrofit total cost especially in sub-ambient processes.

Integrating spatial optimization and cellular automata for evaluating urban change

Urban growth and change presents numerous challenges for planners and policy makers. Effective and appropriate strategies for managing growth and change must address issues of social, environmental and economic sustainability. Doing so in practical terms is a difficult task given the uncertainty associated with likely growth trends not to mention the uncertainty associated with how social and environmental structures will respond to such change. An optimization based approach is developed for evaluating growth and change based upon spatial restrictions and impact thresholds. The spatial optimization model is integrated with a cellular automata growth simulation process. Application results are presented and discussed with respect to possible growth scenarios in south east Queensland, Australia.

Design and implementation of a windows-based parallel computing environment for large scale optimization

A parallel computing environment to support optimization of large-scale engineering systems is designed and implemented on Windows-based personal computer networks, using the master-worker model and the Parallel Virtual Machine (PVM). It is involved in decomposition of a large engineering system into a number of smaller subsystems optimized in parallel on worker nodes and coordination of subsystem optimization results on the master node. The environment consists of six functional modules, i.e. the master control, the optimization model generator, the optimizer, the data manager, the monitor, and the post processor. Object-oriented design of these modules is presented. The environment supports steps from the generation of optimization models to the solution and the visualization on networks of computers. User-friendly graphical interfaces make it easy to define the problem, and monitor and steer the optimization process. It has been verified by an example of a large space truss optimization. (C) 2004 Elsevier Ltd. All rights reserved.

Population-based continuous optimization, probabilistic modelling and mean shift

Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous theoretical work, a continuous version of the population-based incremental learning algorithm, and the generalized mean shift clustering framework. Experimental results are presented that demonstrate the dynamics of the new algorithm on a set of simple test problems.

Optimization of pipeline transport for CO2 sequestration

Coal fired power generation will continue to provide energy to the world for the foreseeable future. However, this energy use is a significant contributor to increased atmospheric CO2 concentration and, hence, global warming. Capture and disposal Of CO2 has received increased R&D attention in the last decade as the technology promises to be the most cost effective for large scale reductions in CO2 emissions. This paper addresses CO2 transport via pipeline from capture site to disposal site, in terms of system optimization, energy efficiency and overall economics. Technically, CO2 can be transported through pipelines in the form of a gas, a supercritical. fluid or in the subcooled liquid state. Operationally, most CO2 pipelines used for enhanced oil recovery transport CO2 as a supercritical fluid. In this paper, supercritical fluid and subcooled liquid transport are examined and compared, including their impacts on energy efficiency and cost. Using a commercially available process simulator, ASPEN PLUS 10.1, the results show that subcooled liquid transport maximizes the energy efficiency and minimizes the Cost Of CO2 transport over long distances under both isothermal and adiabatic conditions. Pipeline transport of subcooled liquid CO2 can be ideally used in areas of cold climate or by burying and insulating the pipeline. In very warm climates, periodic refrigeration to cool the CO2 below its critical point of 31.1 degrees C, may prove economical. Simulations have been used to determine the maximum safe pipeline distances to subsequent booster stations as a function of inlet pressure, environmental temperature and ground level heat flux conditions. (c) 2005 Published by Elsevier Ltd.

Optimization of integrated chemical-biological degradation of a reactive azo dye using response surface methodology

The integrated chemical-biological degradation combining advanced oxidation by UV/H2O2 followed by aerobic biodegradation was used to degrade C.I. Reactive Azo Red 195A, commonly used in the textile industry in Australia. An experimental design based on the response surface method was applied to evaluate the interactive effects of influencing factors (UV irradiation time, initial hydrogen peroxide dosage and recirculation ratio of the system) on decolourisation efficiency and optimizing the operating conditions of the treatment process. The effects were determined by the measurement of dye concentration and soluble chemical oxygen demand (S-COD). The results showed that the dye and S-COD removal were affected by all factors individually and interactively. Maximal colour degradation performance was predicted, and experimentally validated, with no recirculation, 30 min UV irradiation and 500 mg H2O2/L. The model predictions for colour removal, based on a three-factor/five-level Box-Wilson central composite design and the response surface method analysis, were found to be very close to additional experimental results obtained under near optimal conditions. This demonstrates the benefits of this approach in achieving good predictions while minimising the number of experiments required. (c) 2006 Elsevier B.V. All rights reserved.

Optimization of facility location-allocation problem in customer-driven supply chain

This paper develops and applies an integrated multiple criteria decision making approach to optimize the facility location-allocation problem in the contemporary customer-driven supply chain. Unlike the traditional optimization techniques, the proposed approach, combining the analytic hierarchy process (AHP) and the goal programming (GP) model, considers both quantitative and qualitative factors, and also aims at maximizing the benefits of deliverer and customers. In the integrated approach, the AHP is used first to determine the relative importance weightings or priorities of alternative locations with respect to both deliverer oriented and customer oriented criteria. Then, the GP model, incorporating the constraints of system, resource, and AHP priority is formulated to select the best locations for setting up the warehouses without exceeding the limited available resources. In this paper, a real case study is used to demonstrate how the integrated approach can be applied to deal with the facility location-allocation problem, and it is proved that the integrated approach outperforms the traditional costbased approach.

Optimization of protein crystallization:the OptiCryst project

Protein crystallization has gained a new strategic and commercial relevance in the postgenomic era due to its pivotal role in structural genomics. Producing high quality crystals has always been a bottleneck to efficient structure determination, and this problem is becoming increasingly acute. This is especially true for challenging, therapeutically important proteins that typically do not form suitable crystals. The OptiCryst consortium has focused on relieving this bottleneck by making a concerted effort to improve the crystallization techniques usually employed, designing new crystallization tools, and applying such developments to the optimization of target protein crystals. In particular, the focus has been on the novel application of dual polarization interferometry (DPI) to detect suitable nucleation; the application of in situ dynamic light scattering (DLS) to monitor and analyze the process of crystallization; the use of UV-fluorescence to differentiate protein crystals from salt; the design of novel nucleants and seeding technologies; and the development of kits for capillary counterdiffusion and crystal growth in gels. The consortium collectively handled 60 new target proteins that had not been crystallized previously. From these, we generated 39 crystals with improved diffraction properties. Fourteen of these 39 were only obtainable using OptiCryst methods. For the remaining 25, OptiCryst methods were used in combination with standard crystallization techniques. Eighteen structures have already been solved (30% success rate), with several more in the pipeline.

Multiple criteria optimization of contemporary logistics distribution network problems

In the contemporary customer-driven supply chain, maximization of customer service plays an equally important role as minimization of costs for a company to retain and increase its competitiveness. This article develops a multiple-criteria optimization approach, combining the analytic hierarchy process (AHP) and an integer linear programming (ILP) model, to aid the design of an optimal logistics distribution network. The proposed approach outperforms traditional cost-based optimization techniques because it considers both quantitative and qualitative factors and also aims at maximizing the benefits of deliverer and customers. In the approach, the AHP is used to determine the relative importance weightings or priorities of alternative warehouses with respect to some critical customer-oriented criteria. The results of AHP prioritization are utilized as the input of the ILP model, the objective of which is to select the best warehouses at the lowest possible cost. In this article, two commercial packages are used: including Expert Choice and LINDO.

Self-organization of Functioning Process of Expert System with the Use of Subject Domain Ontology

Optimization of design, creation, functioning and accompaniment processes of expert system is the important problem of artificial intelligence theory and decisions making methods techniques. In this paper the approach to its solving with the use of technology, being based on methodology of systems analysis, ontology of subject domain, principles and methods of self-organisation, is offered. The aspects of such approach realization, being based on construction of accordance between the ontology hierarchical structure and sequence of questions in automated systems for examination, are expounded.

Modeling and optimization of cascaded erbium and holmium doped fluoride fiber lasers

Numerical modeling of cascade erbium-doped and holmium-doped fluoride fiber lasers is presented. Fiber lengths were optimized for cascade lasers that had fixed or free-running wavelengths using all known spectroscopic parameters. The performance of the cascade laser was tested against dopant concentration, energy transfer process, heat generation, output coupling, and pump schemes. The results suggest that the slope efficiencies and thresholds for both transitions increase with increasing Ho3+ or Er3+ concentration with the slope efficiency stabilizing after 1 mol% rare earth doping. The heat generation in the Ho3+-based system is lower compared to the Er 3+-based system at low dopant concentration as a result of the lower rates of multiphonon relaxation. Decreasing the output coupling for the upper (∼3 μm) transition decreases the threshold of the lower transition and the upper transition benefits from decreasing the output coupling for the lower transition for both cascade systems. The highest slope efficiency was achieved under counter-propagating pump conditions. Saturation of the output power occurs at comparatively higher pump power with dilute Er3+ doping compared with heavier doping. Overall, we show that the cascade Ho3+ -doped fluoride laser is the best candidate for high power output because of its higher slope efficiency and lower temperature excursion of the core and no saturation of the output. © 2013 IEEE.

A dynamic feature information model for integrated manufacturing planning and optimization

This paper presents a new, dynamic feature representation method for high value parts consisting of complex and intersecting features. The method first extracts features from the CAD model of a complex part. Then the dynamic status of each feature is established between various operations to be carried out during the whole manufacturing process. Each manufacturing and verification operation can be planned and optimized using the real conditions of a feature, thus enhancing accuracy, traceability and process control. The dynamic feature representation is complementary to the design models used as underlining basis in current CAD/CAM and decision support systems. © 2012 CIRP.

Operation sequencing optimization for five-axis prismatic parts using a particle swarm optimization approach

Operation sequencing is one of the crucial tasks in process planning. However, it is an intractable process to identify an optimized operation sequence with minimal machining cost in a vast search space constrained by manufacturing conditions. Also, the information represented by current process plan models for three-axis machining is not sufficient for five-axis machining owing to the two extra degrees of freedom and the difficulty of set-up planning. In this paper, a representation of process plans for five-axis machining is proposed, and the complicated operation sequencing process is modelled as a combinatorial optimization problem. A modern evolutionary algorithm, i.e. the particle swarm optimization (PSO) algorithm, has been employed and modified to solve it effectively. Initial process plan solutions are formed and encoded into particles of the PSO algorithm. The particles 'fly' intelligently in the search space to achieve the best sequence according to the optimization strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particle movements to form a modified PSO algorithm. A case study used to verify the performance of the modified PSO algorithm shows that the developed PSO can generate satisfactory results in optimizing the process planning problem. © IMechE 2009.

Fenton chemistry-based detemplation of an industrially relevant microcrystalline beta zeolite. Optimization and scaling-up studies

A mild template removal of microcrystalline beta zeolite, based on Fenton chemistry, was optimized. Fenton detemplation was studied in terms of applicability conditions window, reaction rate and scale up. TGA and CHN elemental analysis were used to evaluate the detemplation effectiveness, while ICP, XRD, LPHR-Ar physisorption, and 27Al MAS NMR were applied to characterize the structure and texture of the resulting materials. The material properties were compared to calcination. By understanding the interplay of relevant parameters of the Fenton chemistry, the process can be optimized in order to make it industrially attractive for scale-up. The H2O2 utilization can be minimized down to 15 mL H2O2/g (88 °C, 30 ppm Fe), implying a high solid concentration and low consumption of H2O2. When Fe concentration must be minimized, values as low as 5 ppm Fe can be applied (88 °C, 30 mL H2O2/g), to achieve full detemplation. The reaction time to completeness can be reduced to 5 h when combining a Fe-oxalate catalyst with UV radiation. The protocol was scaled up to 100 times larger its original recipe. In terms of the material's properties, the scaled material is structurally comparable to the calcined counterpart (comparable Si/Al and XRD patterns), while it displays benefits in terms of texture and Al-coordination, the latter with full preservation of the tetrahedral Al

Uniform design for the optimization of Al2O3 nanofilms produced by electrophoretic deposition

Surface modification by means of nanostructures is of interest to enhance boiling heat transfer in various applications including the organic Rankine cycle (ORC). With the goal of obtaining rough and dense aluminum oxide (Al2O3) nanofilms, the optimal combination of process parameters for electrophoretic deposition (EPD) based on the uniform design (UD) method is explored in this paper. The detailed procedures for the EPD process and UD method are presented. Four main influencing conditions controlling the EPD process were identified as nanofluid concentration, deposition time, applied voltage and suspension pH. A series of tests were carried out based on the UD experimental design. A regression model and statistical analysis were applied to the results. Sensitivity analyses of the effect of the four main parameters on the roughness and deposited mass of Al2O3 films were also carried out. The results showed that Al2O3 nanofilms were deposited compactly and uniformly on the substrate. Within the range of the experiments, the preferred combination of process parameters was determined to be nanofluid concentration of 2 wt.%, deposition time of 15 min, applied voltage of 23 V and suspension pH of 3, yielding roughness and deposited mass of 520.9 nm and 161.6 × 10− 4 g/cm2, respectively. A verification experiment was carried out at these conditions and gave values of roughness and deposited mass within 8% error of the expected ones as determined from the UD approach. It is concluded that uniform design is useful for the optimization of electrophoretic deposition requiring only 7 tests compared to 49 using the orthogonal design method.