Integration of modular process simulators under the Generalized Disjunctive Programming framework for the structural flowsheet optimization

The optimization of chemical processes where the flowsheet topology is not kept fixed is a challenging discrete-continuous optimization problem. Usually, this task has been performed through equation based models. This approach presents several problems, as tedious and complicated component properties estimation or the handling of huge problems (with thousands of equations and variables). We propose a GDP approach as an alternative to the MINLP models coupled with a flowsheet program. The novelty of this approach relies on using a commercial modular process simulator where the superstructure is drawn directly on the graphical use interface of the simulator. This methodology takes advantage of modular process simulators (specially tailored numerical methods, reliability, and robustness) and the flexibility of the GDP formulation for the modeling and solution. The optimization tool proposed is successfully applied to the synthesis of a methanol plant where different alternatives are available for the streams, equipment and process conditions.

Integration of different models in the design of chemical processes: Application to the design of a power plant

With advances in the synthesis and design of chemical processes there is an increasing need for more complex mathematical models with which to screen the alternatives that constitute accurate and reliable process models. Despite the wide availability of sophisticated tools for simulation, optimization and synthesis of chemical processes, the user is frequently interested in using the ‘best available model’. However, in practice, these models are usually little more than a black box with a rigid input–output structure. In this paper we propose to tackle all these models using generalized disjunctive programming to capture the numerical characteristics of each model (in equation form, modular, noisy, etc.) and to deal with each of them according to their individual characteristics. The result is a hybrid modular–equation based approach that allows synthesizing complex processes using different models in a robust and reliable way. The capabilities of the proposed approach are discussed with a case study: the design of a utility system power plant that has been decomposed into its constitutive elements, each treated differently numerically. And finally, numerical results and conclusions are presented.

Mathematical programming model for heat exchanger design through optimization of partial objectives

Mathematical programming can be used for the optimal design of shell-and-tube heat exchangers (STHEs). This paper proposes a mixed integer non-linear programming (MINLP) model for the design of STHEs, following rigorously the standards of the Tubular Exchanger Manufacturers Association (TEMA). Bell–Delaware Method is used for the shell-side calculations. This approach produces a large and non-convex model that cannot be solved to global optimality with the current state of the art solvers. Notwithstanding, it is proposed to perform a sequential optimization approach of partial objective targets through the division of the problem into sets of related equations that are easier to solve. For each one of these problems a heuristic objective function is selected based on the physical behavior of the problem. The global optimal solution of the original problem cannot be ensured even in the case in which each of the sub-problems is solved to global optimality, but at least a very good solution is always guaranteed. Three cases extracted from the literature were studied. The results showed that in all cases the values obtained using the proposed MINLP model containing multiple objective functions improved the values presented in the literature.

Multi-objective optimization of environmentally conscious chemical supply chains under demand uncertainty

In this work, we analyze the effect of demand uncertainty on the multi-objective optimization of chemical supply chains (SC) considering simultaneously their economic and environmental performance. To this end, we present a stochastic multi-scenario mixed-integer linear program (MILP) with the unique feature of incorporating explicitly the demand uncertainty using scenarios with given probability of occurrence. The environmental performance is quantified following life cycle assessment (LCA) principles, which are represented in the model formulation through standard algebraic equations. The capabilities of our approach are illustrated through a case study. We show that the stochastic solution improves the economic performance of the SC in comparison with the deterministic one at any level of the environmental impact.

Large scale optimization of a sour water stripping plant using surrogate models

In this work, we propose a new methodology for the large scale optimization and process integration of complex chemical processes that have been simulated using modular chemical process simulators. Units with significant numerical noise or large CPU times are substituted by surrogate models based on Kriging interpolation. Using a degree of freedom analysis, some of those units can be aggregated into a single unit to reduce the complexity of the resulting model. As a result, we solve a hybrid simulation-optimization model formed by units in the original flowsheet, Kriging models, and explicit equations. We present a case study of the optimization of a sour water stripping plant in which we simultaneously consider economics, heat integration and environmental impact using the ReCiPe indicator, which incorporates the recent advances made in Life Cycle Assessment (LCA). The optimization strategy guarantees the convergence to a local optimum inside the tolerance of the numerical noise.

Investigative framework for research into the design of manufacturing processes

This paper is based a major research project run by a team from the Innovation, Design and Operations Management Research Unit at the Aston Business School under SERC funding. International Computers Limited (!CL), the UK's largest indigenous manufacturer of mainframe computer products, was the main industrial collaborator in the research. During the period 1985-89 an integrated production system termed the "Modular Assembly Cascade'' was introduced to the Company's mainframe assembly plant at Ashton-under-Lyne near Manchester. Using a methodology primarily based upon 'participative observation', the researchers developed a model for analysing this manufacturing system design called "DRAMA". Following a critique of the existing literature on Manufacturing Strategy, this paper will describe the basic DRAMA model and its development from an industry specific design methodology to DRAMA II, a generic model for studying organizational decision processes in the design and implementation of production systems. From this, the potential contribution of the DRAMA model to the existing knowledge on the process of manufacturing system design will be apparent.

An investigation of production workers' performance variations and the potential impact of attitudes

In most manufacturing systems the contribution of human labour remains a vital element that affects overall performance and output. Workers’ individual performance is known to be a product of personal attitudes towards work. However, in current system design processes, worker performance variability is assumed to be largely insignificant and the potential impact of worker attitudes is ignored. This paper describes a field study that investigated the extent to which workers’ production task cycle times vary and the degree to which such variations are associated with attitude differences. Results show that worker performance varies significantly, much more than is assumed by contemporary manufacturing system designers and that this appears to be due to production task characteristics. The findings of this research and their implications are discussed.

Techno-economic assessment and uncertainty analysis of thermochemical processes for second generation biofuels

Biomass-To-Liquid (BTL) is one of the most promising low carbon processes available to support the expanding transportation sector. This multi-step process produces hydrocarbon fuels from biomass, the so-called “second generation biofuels” that, unlike first generation biofuels, have the ability to make use of a wider range of biomass feedstock than just plant oils and sugar/starch components. A BTL process based on gasification has yet to be commercialized. This work focuses on the techno-economic feasibility of nine BTL plants. The scope was limited to hydrocarbon products as these can be readily incorporated and integrated into conventional markets and supply chains. The evaluated BTL systems were based on pressurised oxygen gasification of wood biomass or bio-oil and they were characterised by different fuel synthesis processes including: Fischer-Tropsch synthesis, the Methanol to Gasoline (MTG) process and the Topsoe Integrated Gasoline (TIGAS) synthesis. This was the first time that these three fuel synthesis technologies were compared in a single, consistent evaluation. The selected process concepts were modelled using the process simulation software IPSEpro to determine mass balances, energy balances and product distributions. For each BTL concept, a cost model was developed in MS Excel to estimate capital, operating and production costs. An uncertainty analysis based on the Monte Carlo statistical method, was also carried out to examine how the uncertainty in the input parameters of the cost model could affect the output (i.e. production cost) of the model. This was the first time that an uncertainty analysis was included in a published techno-economic assessment study of BTL systems. It was found that bio-oil gasification cannot currently compete with solid biomass gasification due to the lower efficiencies and higher costs associated with the additional thermal conversion step of fast pyrolysis. Fischer-Tropsch synthesis was the most promising fuel synthesis technology for commercial production of liquid hydrocarbon fuels since it achieved higher efficiencies and lower costs than TIGAS and MTG. None of the BTL systems were competitive with conventional fossil fuel plants. However, if government tax take was reduced by approximately 33% or a subsidy of £55/t dry biomass was available, transport biofuels could be competitive with conventional fuels. Large scale biofuel production may be possible in the long term through subsidies, fuels price rises and legislation.

Optimal input/output reduction in production processes

While conventional Data Envelopment Analysis (DEA) models set targets for each operational unit, this paper considers the problem of input/output reduction in a centralized decision making environment. The purpose of this paper is to develop an approach to input/output reduction problem that typically occurs in organizations with a centralized decision-making environment. This paper shows that DEA can make an important contribution to this problem and discusses how DEA-based model can be used to determine an optimal input/output reduction plan. An application in banking sector with limitation in IT investment shows the usefulness of the proposed method.

Input/output deterioration in production processes

In many real applications of Data Envelopment Analysis (DEA), the decision makers have to deteriorate some inputs and some outputs. This could be because of limitation of funds available. This paper proposes a new DEA-based approach to determine highest possible reduction in the concern input variables and lowest possible deterioration in the concern output variables without reducing the efficiency in any DMU. A numerical example is used to illustrate the problem. An application in banking sector with limitation of IT investment shows the usefulness of the proposed method. © 2010 Elsevier Ltd. All rights reserved.

Reliability modelling and verification of manufacturing processes based on process knowledge management

Reliability modelling and verification is indispensable in modern manufacturing, especially for product development risk reduction. Based on the discussion of the deficiencies of traditional reliability modelling methods for process reliability, a novel modelling method is presented herein that draws upon a knowledge network of process scenarios based on the analytic network process (ANP). An integration framework of manufacturing process reliability and product quality is presented together with a product development and reliability verification process. According to the roles of key characteristics (KCs) in manufacturing processes, KCs are organised into four clusters, that is, product KCs, material KCs, operation KCs and equipment KCs, which represent the process knowledge network of manufacturing processes. A mathematical model and algorithm is developed for calculating the reliability requirements of KCs with respect to different manufacturing process scenarios. A case study on valve-sleeve component manufacturing is provided as an application example of the new reliability modelling and verification procedure. This methodology is applied in the valve-sleeve component manufacturing processes to manage and deploy production resources.

A karcsú (lean) elvek alkalmazásának tapasztalatai az egészségügyi folyamatok fejlesztésében (Experiences of the adaptation of lean management in the development of healthcare processes)

Az elmúlt néhány évben a külföldi sajtóban és szakmai publikációkban egyre többször jelenik meg a „lean egészségügy”, azaz a karcsú menedzsment alkalmazása az egészségügyben mint téma. Habár az ez irányú kutatások még nemzetközi szinten is csak legfeljebb a hajnalukon tartanak, Magyarországon még szinte teljes a sötétség. Ennek a cikknek az a célja, hogy egyrészről felhívja a kutatók, de még inkább a egészségügyi dolgozók, menedzserek figyelmét erre a menedzsmenteszközre és filozófiára, mely új lehetőségeket kínál, másrészről, hogy áttekintést adjon a területen végzett nemzetközi kutatások eredményeiről. A tanulmány ennek megfelelően alapvetően két részre bontható. Az első felében az egészségügyi szolgáltatások helyzetének rövid jellemzése után a karcsú menedzsment alapjait és az egészségügyi szolgáltatásokban való alkalmazásának eszményét mutatja be. A második fele ugyanakkor 16 esettanulmány elemzésén keresztül bemutatja, hogy meddig jutott a világ a „lean egészségügy” ideájának megvalósításában. _______ In the past few years “Lean Healthcare” – the adaptation of lean management into healthcare settings – turns up as a topic often and often in foreign press and the in the professional publications. Although researches at international level in this field are at best at their dawning, in Hungary the darkness is almost complete. This article aims at one side to draw researchers’ and even more healthcare employees’ and managers’ attention to this management tool and philosophy, which offers new possibilities. From the other side to provide an overview of the results of the researches conducted in this field. Reflecting this doubled aim the study is divided into two major sections. In the first part the situation of the health care providers is shortly described followed by the introduction of the basics of the lean management and the idea of applying it into healthcare services. While the second part of the study shows how far the World reached in realizing the idea of “Lean Healthcare” by analyzing 16 cases.

A multi-scale assessment of physiological processes in arctic tundra plants under natural and simulated climate change scenarios

Climate warming is predicted to cause an increase in the growing season by as much as 30% for regions of the arctic tundra. This will have a significant effect on the physiological activity of the vascular plant species and the ecosystem as a whole. The need to understand the possible physiological change within this ecosystem is confounded by the fact that research in this extreme environment has been limited to periods when conditions are most favorable, mid June–mid August. This study attempted to develop the most comprehensive understanding to date of the physiological activity of seven tundra plant species in the Alaskan Arctic under natural and lengthened growing season conditions. Four interrelated lines of research, scaling from cellular signals to ecosystem processes, set the foundation for this study. ^ I established an experiment looking at the physiological response of arctic sedges to soil temperature stress with emphasis on the role of the hormone abscisic acid (ABA). A manipulation was also developed where the growing season was lengthened and soils were warmed in an attempt to determine the maximum physiological capacity of these seven vascular species. Additionally, the physiological capacities of four evergreens were tested in the subnivean environment along with the potential role anthocyanins play in their activity. The measurements were scaled up to determine the physiological role of these evergreens in maintaining ecosystem carbon fluxes. ^ These studies determined that soil temperature differentials significantly affect vascular plant physiology. ABA appears to be a physiological modifier that limits stomatal processes when root temperatures are low. Photosynthetic capacity was limited by internal plant physiological mechanisms in the face of a lengthened growing season. Therefore shifts in ecosystem carbon dynamics are driven by changes in species composition and biomass production on a per/unit area basis. These studies also found that changes in soil temperatures will have a greater effect of physiological processes than would the same magnitude of change in air temperature. The subnivean environment exhibits conditions that are favorable for photosynthetic activity in evergreen species. These measurements when scaled to the ecosystem have a significant role in limiting the system's carbon source capacity. ^

A methodology to measure coordination of business processes

Coordination of business processes is the management of dependencies where dependencies constrain how the tasks are performed. It has been traditionally done in an intuitive fashion, without paying much attention to the coordination load. Coordination load is being defined as the ratio between the time spent on coordination activities and the total task time. Previous efforts to understand and analyze coordination have resulted in mostly qualitative approaches to categorize and recommend coordination strategies. This research seeks to answer two questions: (1) How can we analyze process coordination problems to improve overall performance? (2) What guidance can we provide to reduce the coordination load of the process and consequently improve the organization's performance? Thus, this effort developed a quantitative measure for coordination load of business processes and a methodology to apply such measure. ^ This effort used a management simulation game to have a controlled laboratory environment enabling the manipulation of the task factors variability, analyzability, and interdependence to measure their impact on coordination load. The hypothesis was that the more variable, non-analyzable, and interdependent a process, the higher the coordination load, and that a higher coordination load would have a negative impact on performance. Coordination load was measured via the surrogate coordination time, and performance via profit. ^ A 22 x 31 full factorial design, with two replicates, was run to observe the impact on the variables coordination time and profit. Properly validated spreadsheets and questionnaires were used as data collection instruments for each scenario. The experimental results indicate that lower task analyzability (ρ=0.036) and higher task interdependence (ρ=0.000) lead to higher coordination load, and higher levels of task variability (ρ=0.049) lead to lower performance. However, contrary to the hypotheses postulated by this work, coordination load did not prove to be strong predictor of performance (correlation of -0.086). ^ These findings from the laboratory experiment and other lessons learned were incorporated to develop a quantitative measure, a tool (survey) to use to gather data for the variables in the measures, and a methodology to quantify coordination load of production business processes. The practicality of the methodology is demonstrated with an example.^