Process synthesis and optimisation tools for environmental design: methodology and structure

E L S E V I E R Computers and Chemical Engineering 24 (2000) 1195-1200

Computers & Chemical Engineering

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Process synthesis and optimisation tools for environmental design: methodology and structure

Brett Alexander *, Geoff Barton, Jim Petrie, Jose Romagnoli Department of Chemical Engineering, University of Sydney, Sydney, NSW, 2006 Australia

Abstract

Process design requires the simultaneous satisfaction of environmental, economic and social goals. This invariably requires some trade off between these objectives. The challenge for process design engineers is to develop synthesis and analysis tools, which support this requirement. Process System Engineering (PSE) techniques for multiple objective optimisation have to date focused typically on the optimisation of cost versus the potential for waste minimisation, with the recent inclusion of operability issues. The incorporation of environmental sensitivity into PSE approaches has been less than satisfactory. Much of this stems from the (seeming) difficulty in translating process information to environmental objectives. It is our argument that life cycle assessment (LCA), a methodology for quantifying the full 'cradle-to-grave' impact of industrial processes, can be used to assist in developing environmental objectives for process design and analysis. In this paper, we restrict our analysis to the multiple objective optimisation of environmental and economic objectives. Our approach is demonstrated for the case study of a nitric acid plant, modeled using Hysys ©. The general approach entails the transfer of mass and energy information from the Hysys ¢ model to the optimisation algorithm. Environmental objectives, based on the Hysys © model, are formulated first using a life cycle assessment toolbox. The multi-objective formulation of the process combines economic objectives with the LCA-based environmental objectives. The optimiser routine uses goal programming to identify the Pareto surface of non inferior solutions for this situation, thereby making explicit what trade-off between economic and environmental objectives results from any preferred operating condition. © 2000 Elsevier Science Ltd. All rights reserved.

Keywords: Process design and analysis; Life cycle assessment (LCA); Multi-objective optimisation; Goal programming

I. Introduction

Design and optimisation of a process plant requires satisfaction of economic, environmental and social objectives. The total satisfaction of all of the objectives simultaneously is usually not possible and hence it is necessary to trade off between them. The challenge for the process systems engineering (PSE) community is to develop synthesis and analysis tools that make explicit these trade offs. Additionally it is necessary that these tools are consistent with the general body of knowledge upon which their field of expertise is based.

PSE techniques for multiple objective optimisation have typically been developed around the optimisation of cost and waste/energy minimisation (Rossiter, 1994; Sorin & Paris, 1997) with the recent inclusion of operability issues (Bahri, Bandoni & Romagnoli, 1997).

* Corresponding author.

Over the past decade, attempts have been increasingly made to incorporate environmental sensitivity into PSE approaches (Pistikopoulos, Stefanis & Livingston, 1994; Stefanis, Livingston & Pistikopoulos, 1995) and have generally proven to be less than satisfactory. Much of this stems from the (seeming) difficulty of converting process information into environmental objectives, where these have tended to be oversimplified to satisfy the intellectual and computational constraints of exist- ing tools. The failure of such traditional economic analysis methods to address environmental issues is well-documented (Jackson & Clift, 1998).

Life cycle assessment (LCA) and similar approaches that relate emissions to impacts have found increasing use in recent years as methods for the estimation of environmental impacts as part of process analysis (Aza- pagic & Clift, 1999; Shonnard & Hiew, 1999; Spengler, Geldermann, Hahre, Sieverdingbeck & Rentz, 1998). We propose that as LCA quantifies full 'cradle-to-

0098-1354/00/$ - see front matter © 2000 Elsevier Science Ltd. All rights reserved. PII: S0098-1354(00)00356-2

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grave' impacts of industrial processes it can be used to formulate environmental objectives for process design and analysis. Many adverse effects are obscured if such a broad system boundary is not used. Of course, when looking at process design, there are many of these 'upstream' and 'downstream' impacts which are outside of the design engineer's control. What we are suggest- ing here only is that these can be considered within the design process if LCA (and its attendant databases of environmental impact information) is used to inform the identification of environmental objectives.

The practice of LCA has been divided into four stages namely; (1) goal definition and scoping; (2) inventory analysis; (3) impact assessment and (4) improvement assessment (Heijungs et al., 1992; Fava, Consoli, Denison, Dickson, Mohin, & Vigon, 1993). The initial step of LCA (goal definition and scope) requires the purpose of the study, upon which the identification of system boundaries and assumptions are based and to be defined clearly. In the second step (inventory analysis) material and energy inputs and outputs are quantified for each stage in the process to produce a life cycle inventory (LCI). This procedure is consistent with material and energy balances for the entire process defined within the system boundary. The third stage (impact assessment (IA)) involves quantifying for each of the mass and energy inputs and outputs their extent of resource depletion, human health impacts and broader ecological impacts. The final stage investigates opportunities for process changes to deliver improvements in environmental performance. The rigorous structure of LCA provides an audit trail through which mass and energy balance information is coupled directly to environmental impacts. This means that key environmental concerns of all stakeholders involved in project decision making can be linked to the process design problem in a fairly transparent manner. This makes it easier to formulate environmental objectives as part of a multi-objective optimisation strategy. Indeed, the way in which LCA links to process performance suggests that there is potential to explore formally the incorporation of LCA into PSE approaches to design and analysis. This is our key objective in this paper.

As an initial exercise, we restrict our analysis to a consideration of environmental and economic aspects alone. However, we propose that the optimisation strategy developed here can be extended to include other suitably formulated objectives. The first extension of this will be a consideration of operability issues. A case study of a nitric acid plant has been undertaken to demonstrate this approach. The question that arises in the design and analysis of the nitric acid process is 'What is the preferred operating environment to ensure the trade off between economic and environmental objectives is an efficient one in the Pareto sense?' An example of the potential trade offs in this case study

follows: high-pressure operation gives better absorber efficiency and therefore less nitrogen oxides in the tail gas stream. Nitrogen oxide emissions contribute to environmental impacts associated with acidification, photochemical smog and ozone depletion. At the same time, this high-pressure operation comes at the cost of greater energy requirements, with, amongst others, an increased contribution to greenhouse gases emissions if that energy is derived from fossil fuels. This picture suggests that there exists an obvious trade off between different environmental issues, as well as between economic and environmental performance. It is the explo- ration of these trade-offs that we seek to demonstrate here.

2. Framework and methodology

The process is modeled in Hysys ©, to obtain mass and energy information. Hysys © is selected as the preferred modeling tool as it includes steady-state optimisation, is extendable to dynamics and is readily interfaced to a range of third party packages (including advanced control and commercial distributed control systems DCS).

Mass and energy information is transferred from the Hysys ~ model to Excel © using an object link and embed (OLE) link. OLE is a tool that enables applications to expose information/data constructed within them to other applications to support automation. The ex- tracted mass and energy data provide the basis for a life cycle inventory (LCI), from which, in turn, an environmental impact profile can be developed using accepted methodology. In this paper, we formulate a set of environmental objectives directly from the impact potential information generated by the LCA exercise. An economic model of the nitric acid process, including details of all capital and operating costs, was formulated in MS-Excel. A separate economic objective function is formulated based on the rate of return.

Goal programming, a multi-objective optimisation technique, has been used to solve the multi-objective problem to identify the Pareto surface for this situation. Goal programming was selected as the optimisation technique as it is a method suitable for such applications where the possible values of a criteria are a continuum (Stewart, 1992) - - in other words where the individual terms of each objective function are defined by continuous variables, as is the case here. The Pareto surface is the set of non-inferior solutions and each such solution within the set has the property that it is not possible to improve any of the objectives without simultaneously degrading the value of another. The ability to identify the Pareto surface for the given situation enables the trade-off between economic and environmental objectives that result from selection of a

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specific operating condition to be made explicit. The algorithm for the goal programming technique is presented below.

Minimise

A schematic of the proposed methodology is presented in Fig. 1; it demonstrates the structure of the approach, the inter-linking of software tools and the information flow.

k

Y. w,l il i=1

where 8 i=g i - a i ; ; gi, selected goal for the specific criterion; ai: performance score in a specific criterion; wi: weights (sum up to 1).

In choosing performance criteria it is necessary to consider the ability of the decision maker to express meaningful goals for them. Weights are an expression of the decision maker's preference for a given criterion relative to all others. Different weighting protocols exist, though these will not be dwelt with here (see von Winterfeldt & Edwards, 1986; Keeney, 1992). A start- ing set of design variables is fed to the process model and a value for the multi-objective algorithm is calculated.

This value is tested for convergence. If the test fails, a new set of design variables is obtained using a New- ton gradient search method, and fed back into the process simulation model. This procedure is repeated until the optimisation algorithm converges. Tolerance levels for the convergence are determined by perform- ing a sensitivity analysis around the number of signifi- cant figures required in the multi-objective output value to provide the desired accuracy for the design variables.

/ 'NON CONVERGED"

DESIGN VARIABLES

ENVIRONMENTAL

P R O C E S S M O D E L

HYSYS . . . . . . . . . . . . . . . . . . . . . . . ,

MASS & ENERGY

INFORMATION

~ XCF-.,L

C~IT.~ & i OPERATING COSTS

I MUL'I'I(~JEffrI~ ALGOFIrl3t'~

4, "CONVERGED"

DESIGN VARIABLES

Fig. 1. Structure of approach.

3. Description of the case study

A medium-pressure nitric acid plant using Uhde Technology was modeled in the process simulation package Hysys ¢. We have used this case study previously to demonstrate the use of trade off curves (Kniel, Delmarco & Petrie, 1996) and it provides a good source of real data. The process was modeled between pressures of 3.4-4.4 bar and produced approximately 30 000 kg h - 1 of 56% nitric acid. The design variables selected for this study are the converter and absorber pressures, and process water flow used in NO2 absorption.

The LCA study was constrained to a 'cradle to gate' analysis, i.e. impacts related to life-cycle activities downstream of acid production are excluded from the analysis. This is appropriate as our focus is on design for a fixed product specification. Activities upstream of acid production (e.g. provision of feedstocks and utili- ties) must be considered however as the magnitude of environmental impacts to which these give rise are affected by process conditions within the nitric acid plant. In this work, 'upstream' burdens are derived from a commercial LCA databases.

The use of LCA tools in environmental assessment is criticised often because of its perceived 'data intensity', particularly when dealing with detailed chemical processes. We have developed previously a method for structuring process flowsheets which minimises the complexity of a given flowsheet required for an LCA, whilst still retaining the requisite information required for process design (Stewart & Petrie, 1996). This approach has been shown to be particularly useful when following a hierarchical approach to process synthesis (Stewart & Petrie, 1999). In this way, information requirements remain manageable.

Impact assessment (IA) calculations were performed using accepted LCA methodology, by which environmental burdens of individual stream components are derived relative to a particular reference component for each recognised environmental problem.

The multi-objective optimisation technique used in the case study is weighted goal programming. The best performances for each of the criteria over the specified operating ranges are used as the goals for the multi-objective optimisation. Externally imposed goals were not used, as goals that pertain to values outside the possible performance ranges of each criteria for a given optimisation problem tend to distort the analysis results. Throughout the study the goals for the individual ob-

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Comparison of Optlmisod Operation for Individual Objective Functions a0

o

" I ~- ~" aAcid~icate. =U O [•Greenhouse- dlrecl

[ .9.. u Lr'l Rate of Return = o K

nO A clcllflcotlo n M Inlmb ~ Greenhous e" circe! R ore of R etum M ~lrnl$ ed M Inlml$ ecl

Ob|e©t lve Function Optlmlead

Fig. 2. A comparison the results obtained by individually optimising each of objective functions.

1.00 .1' o ~ m 0.80

~ _~ o.eo 0.40

= 7- ~ 0.20 ~1 o 0 .00

E f f e c t o f Varying the Ratio of the Economlc Attribute relative to an aggregated Environmental Attribute.

• Acidification I

• Greenhouse - direct

I"1 Rate of Return

0.5 : 0.5 0.6 : 0.4 0.67 : 0.33 t .0 : 0.0

Weights (Combined Environmental : Rate of Return)

Fig. 3. Effect of varying the ratio of the economic attribute relative to an aggregated environmental attribute.

jectives were not changed. Weights are given to each of the objectives and then the goal-programming algorithm was minimised to identify the operating point for the given preference, as determined by the ratio of the weights. The process for eliciting weights was not engaged with in this work, but variation of their values was treated via parametric sensitivity studies. The optimisation algorithm is the solver function of Excel (a Newton gradient method).

The objective functions are normalised in terms of production rate of nitric acid. The range of performances in each objective is scaled over a range 0-1, where these limits are defined by 'best' and 'worst' performances, respectively within the specified normal operating ranges of the process model. This is important to ensure unit consistency in any trade off analysis.

To demonstrate this approach, three attributes only were used to formulate objective functions in the optimisation problem. Two of the formulated objectives were environmental attributes and the third was an economic attribute. The Environmental attributes are acidification potential and direct contribution to greenhouse gases. The Economic objective was expressed in terms of the intemal rate of return. The weights applied to the three objective functions were varied to explore the potential of our proposed approach to make explicit the trade off between the environmental and economic criteria.

Three scenarios were investigated. In the first case, each objective function was optimised independently of the others. This equates to assigning weights of zero to these latter two. The second case examines the trade off between the economic objective and (equally weighted)

environmental objectives, whilst the final scenario con- siders trade offs between the environmental objectives.

4. Optimisation of discrete objectives

A comparison of the corresponding trade offs for the three resulting operating points is presented in Fig. 2. These three points represent the outer bounds of the Pareto optimal surface (set of non-inferior solutions) for the specific ranges of the design variables in this study. Fig. 2 indicates that optimisation of the process to achieve best performance (zero) in either of the environmental objectives results both in poor performance of the other environmental objective and the economic objective. This result confirms that the environmental objectives are conflicting with one another. To achieve satisfactory performance in both it will be necessary to trade off one against the other, at the expense of economic return.

5. Aggregating environmental objectives

As a second example of the methodology, the environmental objectives were aggregated into a single objective. An objectives' hierarchy was formulated that dictated how the performance criteria were summed. The case examined here is a two tier hierarchical structure where, on the first tier, environmental criteria are summed with equal weights to provide a combined environmental score. This combined environmental

B. Alexander et al . / Computers and Chemical Engineering 24 (2000) 1195-1200 1199

score is then traded off against the economic score. The ratio of the weight of the combined environment

objective function to the economic objective function was varied to observe the effect of the outcome on the performances of the three performance attributes.

Fig. 3 presents the results of this case. The figure shows that for weights of greater than 0.34 given to the economic objective, the value for the economic function is zero (complete satisfaction of the economic objective), while the performance scores of the other two objectives change very little as this weight becomes bigger. This suggests that, in situations where the economic weight is greater than 0.34, the optimisation is dominated by process economics.

Comparison of the two-tier decision hierarchy to its equivalent single tier structure, as in Fig. 4, reveals the reason for this dominance. The two-tier structure has an economic weight of 0.34 to a combined environmental objective weight of 0.66. This suggests that the combined environment objective is almost twice as important as the economic and yet the economics dom- inate. Its equivalent single tier structure has an economic weight of 0.34 while each of the two individual environment objectives have weights of 0.33 and so the economic weight is greater than either of the individual environmental weights and thus the domination. Whilst this result may be intuitively obvious, it is worth stating here in relation to the objectives hierarchy structure. The exercise of structuring the design problem in terms of multiple objectives (and stipulating the level at which various trade offs are envisaged) is a key task for design engineers, but one which has hitherto been given scant attention.

We have shown too that changing the relative importance of the environmental functions in the aggregation

can alter the value of the weight at which the optimisation swings to an economic dominance. This suggests that it is important to assess the real importance of environmental criteria by adequate canvassing of all stakeholder communities which are likely to be affected by the outcome of the design decision making, and ensuring that this value set is used as the basis for the weighting protocol.

6. Trade off analysis

Fig. 5 shows three cases where the economic weight is held at a constant value of 0.33 and the relative weights of the two environmental attributes are varied. The ratios of the relative weights of greenhouse to acidification used are 1:1, 1:3, and 3:1.

A comparison of the result when the ratio of the weights of greenhouse to acidification is 1:1 to the case of 1:3 shows how an improvement is made to the economic performance by trading off greenhouse against acidification impact potentials.

This ability to trade off between environmental attributes enables more satisfactory performance in an environmental attribute of greater concern, and better economic performance, at the expense of an environmental attribute that is of lesser concern.

7. Conclusions

It has been demonstrated that linking LCA Method- ology with Process Simulation tools, in a multi objective optimisation framework based on goal

0.34 ] 0.66 Economic Combined Environment [ 0.34 I 0.33 I 0.33

0.5 I 0.5 Economic Acidification Greenhouse

Acidification Greenhouse Equivalent Single Tier Structure

Two Tier Structure

Fig. 4. Relationship between objectives' hierarchy and weighting bias.

t,=.~- _,g

-~se I

Trading off the Two Environmental Objective Functions

• Acidification i

• Greenhouse - direct

[r'lRate of Return t

1 :1 3 :1 1 : 3

Weights (Greenhouse-direct / Acidif ication)

Fig. 5. Investigating the effect of trading off environmental objectives.

1200 B. Alexander et al . / Computers and Chemical Engineering 24 (2000) 1195-1200

programming, allows the trade offs between environmental and economic objectives to be made explicit.

We have shown too that, in the development of objectives' hierarchies, it is important to consider how their structure can influence the outcome of a decision. In order to prevent undue bias, there is a need to understand the relationship between the level of trade off required, and the way in which weighting is carried out. Whilst not engaging with weighting protocols directly, we have suggested that the audit trail capacity of LCA, when linked to rigorous process analysis tools, should allow for transparent elicitation of preferences by the decision maker and other stakeholders party to the design decision. This work has highlighted too the ability for design engineers to consider environmental impacts, which accrue (outside of their usual operating domain) from upstream processes.

In cases where the preferred performance of different environmental attributes occurs at significantly different operating points for a given process, the separation of these attributes into individual objectives enables them to be traded off against one another. This can produce more satisfactory performance in an environmental attribute of greater concern and better economic performance at the expense of an environmental attribute that is of less concern.

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Process synthesis and optimisation tools for environmental design: methodology and structure

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