Methodology for Environmental Risk Assessment of Industrial

3694

Ind. Eng. Chem. Res. 1997, 36, 3694-3707

Methodology for Environmental Risk Assessment of Industrial Nonroutine Releases Stavros K. Stefanis and Efstratios N. Pistikopoulos* Department of Chemical Engineering, Centre for Process Systems Engineering, Imperial College, London SW7 2BY, U.K.

While increasing social concern and strict legislation have resulted in expanding the conventional design objectives of profitability to include environmental impact and operability aspects, traditional practices regarding environmental risk assessment (ERA) have mainly focused on providing qualitative guidelines to evaluate the likelihood and consequence of undesired events to the environment. By linking process reliability considerations to environmental impact analysis within a process optimization framework, this work presents a systematic method for the quantification and at source minimization of combined adverse environmental effects of routine (i.e., waste water effluent streams) and nonroutine releases (i.e., leaks, emissions from equipment breakdown). Trade-offs are explored regarding cost and routine/nonroutine environmental impact objectives, while opportunities for effective maintenance strategies are identified. The steps of the theoretical analysis and the potential of the proposed methodology are illustrated with two example problems, a simplified chemical reaction-separation scheme and a methane chlorination process. 1. Introduction Environmental risk assessment is typically concerned with the estimation of the damage caused to humans by hazardous pollutants and is traditionally defined as the likelihood of an adverse health effect, such as a carcinogenic death, due to an exposure to an environmental hazard (Lapp, 1991). Accidents and major disasters such as the Seveso incident in Northern Italy highlighted the additional need to address the impact of such incidents on the environment, which, for example, prompted the European Commission to introduce the first legislative framework for controlling major human hazards, entitled the “Seveso Directive” (1996). Most of the available methods for assessing environmental risk are mainly qualitative such as checklists and networks (HMSO, 1995). To differentiate from human risks, environmental risk assessment considers the various components of the environment such as air, water, and soil. Christou (1996) proposed a framework for developing an integrated approach for environmental risk assessment, which relies on qualitative hazard identification techniques (such as HAZOP and FMEA; see Montague (1990)). His approach focuses on postrelease calculations (i.e., fate of pollutants and their health effects) rather than the actual source of pollution and its cause in process, either intended or unintended. Risk-related events (like accidents, off-spec production, etc.) have been incorporated quantitatively in formal environmental impact assessment by Aelion et al. (1995) through the idea of frequency/environmental load curve, which preserves aggregate information about accidental releases. Their framework is mostly suitable as a prescreening environmental analysis tool since it does not give the designer any explicit information on how to prevent the plant from undesired environmental risks and/or how to alter the plant operation so that a costly plant shutdown be avoided. Environmental risk management is an important component of risk estimation that has not been exten* To whom correspondence should be addressed. E-mail: [email protected]. S0888-5885(96)00781-6 CCC: $14.00

sively studied so far. Existing environmental risk techniques perform the environmental risk management at the post assessment level in an iterative fashion (HMSO, 1995) using health and not environmental indicators (Sarigiannis and Volta, 1996) typically based on operational aspects of the plant in question; however, the decision making to minimize and manage risks often requires extensive iterative effects. There is considerable scope for the development of more efficient techniques that will allow for the systematic study of the effects of process design, operating parameters, and unexpected events on environmental risk and its management. In this respect, optimization tools can provide a sound basis for the efficient integration of environmental risk assessment so as to arrive at process design and operational decisions that minimize environmental risk without entailing excessive costs. Recently, we introduced a methodology for environmental impact minimization (MEIM) that embeds life cycle analysis principles (LCA) within an optimization framework for continuous as well as batch processes (Pistikopoulos et al., 1994; Stefanis et al., 1996) to quantify the environmental impact of routine releases. The main steps of MEIM include the following: (i) definition of a process system boundary, (ii) environmental impact assessment on a short- or long-term basis, and (iii) incorporation of environmental impact criteria explicitly as process design objectives together with economics in a multiobjective optimization setting. MEIM is an effective tool for a rigorous assessment of the interaction between industrial technology and the environment, helping identify design and operation options to reduce pollution at source. However, since the quantification of the environmental load is currently limited only to routine release scenarios, environmental degradation caused by unexpected or nonroutine events such as equipment breakdown, measurement errors etc. cannot be quantified. Such a quantification and minimization of nonroutine releases is particulraly desired for economic (incentive for minimizing the costs of material lost), legislative (new directives imposed pose strict limits on nonroutine releases), and safety reasons. © 1997 American Chemical Society

Ind. Eng. Chem. Res., Vol. 36, No. 9, 1997 3695

Figure 1. Risk frequency graph.

Figure 2. Process flowsheet of motivating example.

To address some of the above issues, the objectives of this paper are as follows: (i) to quantify principles of MEIM so as to provide an integrated and rigorous framework to assess in a systematic way the adverse effects of industrial processes on ecosystems during ab normal conditions, by extending the principles of MEIM, (ii) to study the effects of plant design and operation on the environmental impact of nonroutine releases, and (iii) to establish the fundamental theory and computational tools to arrive at cost optimal designs featuring minimum environmental risk via the use of multiobjective optimization techniques.

Table 1. Process Specifications for Motivating Example

2. Nonroutine Releases: Issues in Risk and Environmental Impact Assessment

two isothermal reactions take place for the production of chemical C from reactant gases A, B, and D according to the following reaction scheme

A key characteristic of nonroutine releases is that they are often related to equipment failures and the probabilistic occurrence of external events, such as unexpected leaks and human errors. As shown in the risk frequency graph presented in Figure 1, nonroutine releases can significantly influence the environmental damage related to a process system. Unlike extreme cases such as major accidents (occurring at very low frequencies with serious consequences) and routine releases (highly frequent causing minor environmental damage), nonroutine releases, placed in between, often cause moderately severe adverse effects and may therefore result in considerable risk levels. This necessitates the development of an integrated framework that will properly account for nonroutine process waste generation due to “unexpected/undesired” events while simultaneously assessing the environmental impact of routine waste releases. Such a development will require a quantitative means of translating waste emissions attributed to nonroutine releases to environmental impact indices, such as critical air mass, CTAM [)] tn air/h (Habersatter, 1991), as, for example, employed within the methodology for environmental impact minimization (Pistikopoulos et al., 1994). Since the environmental impact of a nonroutine release depends on its probability of occurrence, the machinery of reliability theory can be employed to provide such a formal link, as for example used in the FRAMS methodology (see Thomaidis and Pistikopoulos (1995)). In order to motivate this development and gain some more insight into the nature of the problem, consider the simplified separation process (Pistikopoulos and Thomaidis, 1992) shown in Figure 2. The process involves two reactors, [R1] and [R2], in parallel, where

production rate (kgmol/h) min concentrn of C in the liquid product (% mol) min product temp (K) max reactor volumes (L) 0.01 e purge molar fraction e 0.1

58 82 400 2000

Table 2. Environmental Data

chemical

max acceptable concentrn (mol/tn air)

chemical

max acceptable concentrn (mol/tn air)

A B

0.667 0.167

C D

0.25 0.11

1 A + B f 2C + D 5

(rxn 1)

2 D+BfC 3

(rxn 2)

The process also involves a chiller, a flash drum for the separation, and two compressors, the main compressor for the feed stream (A, B) and the recycle compressor for the gas mixture (A, B, C, D). The process specifications and environmental legislation limits for pollutants released in the purge stream are given in Tables 1 and 2. The routine wastes (at normal steady-state operation) generated by this process are due to the recycle purge stream (see Figure 2). Application of MEIM in this case results in the following: (a) The critical air mass metric CTAM [tn air/h], defined as

CTAM ) mass of emissions w (kgmol w/h)

W ∑w)1 standard limit value (kgmol w/tn air)

is used as a measure of atmospheric damage caused by releases of chemicals A, B, C, and D present in the purge stream. (b) Two solution instances of the two-objective optimization problem for minimum environmental impact and minimum cost respectively are shown in Table 3, where one can see that CTAM values are within the following range [13 480, 15 397] ([)] tn air/h).

3696 Ind. Eng. Chem. Res., Vol. 36, No. 9, 1997 Table 3. Cost/Environmental Optimization Result min environmental impact

min cost

194.7 13 480 169.3 1244.5 89.9

175.2 15 397 117.4 863.3 88.8

annual cost (M$) CTAM (tn air/h) vol of reactor 1 (L) vol of reactor 2 (L) feed flow rate (kgmol/h)

Table 4. Equipment Reliability and Mean Time To Repair Data (β ) 1) CR1, CR2 R (1/h) MTTR (h)

100 000 72

R-1

R-2

70 000 86 000 11 11

EX1, EX2 67 000 72

pump

T-valve

100 000 1.25 × 106 32 16

Table 5. Optimal Design and Operation with No Recycling min cost/ environmental impact annual cost (M$) CTAM (tn air/h) vol of reactor 1 (L) vol of reactor 2 (L) feed flow rate (kgmol/h)

174.5 73 170 574.8 104.4

Suppose now that reliability and availability data are available, as shown in Table 4, with which equipment failures can be modeled by Weibull distribution functions, R(t) ) exp(-t/R)β, where the shape parameter β is used to specify the aging characteristics of the equipment and R is the scale parameter. Given this information, the environmental impact due to equipment failures can then be assessed. For example, if compressor 2 (CR2) of the recycle stream fails, then, as shown in Table 5, this results in a dramatic increase in the environmental impact with a corresponding CTAM of [73 170] (tn air/h), a 440% increase! Clearly, this increase must be balanced with the probability of occurrence of the failure in order to properly quantify the actual environmental impact of such a nonroutine release. Furthermore, the time to repair such a failure (in this case MTTR ) 72 h) and/or the possibility for executing maintenance (condition-based or preventive) become major factors that have a direct effect on the overall environmental impact. Maintenance optimization constitutes an essential element in environmental risk management and prevention, as will be discussed later. Moreover, the interactions between life cycle operation and design optimization are important in attempting to define a process system that is additionally capable of handling equipment failures and other undesired events as well as featuring minimum environmental damage (pollution prevention at source). In the next section, it is shown how the methodology for minimum environmental impact (MEIM, Pistikopoulos et al., 1994) can be extended to include environmental assessment techniques for nonroutine releases and the possibility of executing maintenance. 3. Methodology for Environmental Risk Assessment of Nonroutine Releases In the context of this work, environmental risk (ER) is the measure of potential threats to the environment taking into account that undesired events (scheduled/ unscheduled) will lead to environmental degradation. Qualitatively, environmental risk represents the probability of environmental damage due to undesired

events multiplied by the severity of the environmental degradation. In accordance with the principles of MEIM, the system boundary around the process of interest is first specified. Concentrating mainly on process waste generation, the following framework for minimizing routine and nonroutine releases is proposed (see Figure 3). 3.1. Routine and Nonroutine Emissions Inventory. The process of interest is examined in detail to determine (a) wastes that are regularly emitted into the air, aquatic, or soil environment (b) various nonroutine releases such as 1. accidental releases mainly due to the occurrence of scenarios such as leakage, equipment failure, human error, etc. 2. fugitive emissions that involve small leaks or spills from pumps or flanges and are generally tolerated in industry. 3. releases from process deviations caused during startup, shutdown, and maintenance procedures and also from changes in operating conditions (temperatures, pressures) and various plant parameters such as feed variations 4. episode releases as a result of sudden weather changes or other occurrences. The overall inventory is represented by a waste vector, as shown in Figure 3, which consequently needs to be assessed. 3.2. Assessment of Environmental Damage. All routine and nonroutine releases are often grouped systematically in terms of the environmental damage caused on a short- or long-term basis. For the fully operable state (routine process system status), the environmental impact (EI) vector shown below represents the damage caused to the environment during intended plant operation on a time basis (usually 1 h of operation, ignoring pollutant intermedia partitioning), i.e., the environmental impact of routine releases W

EI )

∑ EIw ) w)1

W

T [CTAMwCTWMwSMDwGWIwPOIwSODIw]process ∑ w)1

(1) comprising indices that measure air pollution CTAM [kg air/h], water pollution CTWM [kg water/h], solid wastes SMD [kg solids/h], global warming GWI [kg CO2/h], photochemical oxidation POI [kg ethylene/h], and stratospheric ozone depletion SODI [kg CFC11/h] for each waste w, depending on current legislation (or toxicity) limits and the mass of pollutant disposed. (These indices rely on the linear contribution assumption of pollutants; extensions to include fate considerations are described elsewhere (Stefanis, 1996).) When an equipment failure or an event that causes the system to significantly deviate from its normal operating status occurs, this defines a new operating state for which a corresponding environmental impact, similar to (1), can be computed. This new operating state will have an associated probability of occurrence, which in general will be a function of equipment reliability models and other data (maintenance, safety events, statistical charts for spills, etc). We denote the set of potential discrete operating states in which a process system can reside over its


Figure 3. Environmental impact assessment of routine/nonroutine releases.

operating time horizon H as state space K, with a corresponding probability Pk(t), k ∈ K, where t denotes time (since the reliability of the processing system is a function of time). A combined environmental impact vector for routine and nonroutine releases, CRNREI, can then be introduced to represent the average environmental damage of a given process design during normal and unexpected operation within a specified time horizon [0,H] as follows. Step 1: (a) Define all operating states K of a process system. (b) Determine corresponding environmental impact vector (EIk), k ∈ K from (1). Step 2: (a) Estimate the reliability (unavailability) of each part of the equipment as a function of time, Rj(t) [Qj(t)]. For example, if Weibull functions are used to describe equipment reliability

Rj(t) )

( )

∫t∞weif Rtj; βj

dt, j ∈ Sk

Qj(t) )

( )

∫0tweif Rtj; βj

P (t) )

Rj(t) ∏Qj(t) ∏ j∈S j

k∈K

j∈Sj

∑

Pk(t)EIk

k∈K

1

∫ EI(t) dt ) H∫H ∑ Pk(t)EIk H H

(5)

Qualitatively, this vector represents the average environmental impact of the process design over all possible system states within a specified time horizon H. Therefore, it measures the overall system environmental performance under both expected and unexpected events. The closer this vector is to the environmental impact vector of the initial state (denoted here as fully operable state o), the lower environmental risk the system conveys. Note that the environmental impact vector attributed to nonroutine releases, NREI, over the time horizon can be easily computed as follows

NREIk ) EIk - EIo k ∈ K

(6)

where EIo is the environmental impact metric corresponding to the fully operable state; i.e., it denotes routine waste releases.

NREI(t) )

NREI )

(4)

1

k∈K

(3)

Step 3: Calculate the environmental impact vector as a function of time, EI(t):

EI(t) )

CRNREI )

dt, j ∈ Sk (2)

where Sk(S h k) is the index set for operational (failed) components of the equipment in state k and R and β are the scale and shape factor of the Weibull function. (b) Determine the probability of each state k, e.g., assuming statistically independent equipment failures: k

Step 4: Determine the combined environmental impact of routine and nonroutine releases for a given time horizon H.

1

Pk(t)NREIk ∑ k∈K 1

∫ NREI(t) dt ) H∫H ∑ Pk(t)NREIk dt H H

(7)

(8)

k∈K

Qualitatively, NREI represents the average environmental impact due to nonroutine releases. For the fully operable state from (6), NREI ) 0, as expected.

3698 Ind. Eng. Chem. Res., Vol. 36, No. 9, 1997

3.3. Design Optimization for Minimum Environmental Impact and Environmental Risk. The combined environmental impact vector, as defined above, provides an accurate estimate of the average environmental performance of the system taking into account both routine and nonroutine releases. In the analysis presented so far, decisions regarding the process design itself (for example, volumes of equipment) were considered fixed. A subsequent question is then how to obtain a minimum cost design while ensuring that the system is capable enough of keeping routine and nonroutine release levels as low as possible. Conceptually, this problem can be posed as the following multiobjective optimization problem:

min [cTy + F(x), CRNREI] x,y

(9)

s.t.

h(x) ) 0 g(x) e 0 By + Cx e D CRNREI(x,y) )

1

Pk(t)EIk dt ∫ H∑ H

∂NREI(t) σ(t) )

k∈K

NREI(x,y) )

1 H

∫H ∑ Pk(t)(EIk - EIo) dt k∈K

x ∈ X, y ∈ Y ∈ {0, 1}n (9) can be reformulated using the -constraint method (Hwang, 1979):

min cTy + F(x) x,y

(10)

s.t.

h(x) ) 0 g(x) e 0 By + Cx e D NREI(x,y) )

1 H

∫H ∑ Pk(t)(EIk - EIo) dt

CRNREI(x,y) )

0, and the inequality constraints g(x) e 0 include design and product specifications, other legislative limits that are typically linear inequalities. An iterative procedure for the solution of the parametric design optimization problem as in (10) is described in Appendix I (see also Figure 18). As shown with the example problems, the solution of such an algorithm yields the pareto curve of economically optimal designs that meet the environmental impact targets (imposed by the value of ). The above problem can be significantly simplified in case environmental costs are assigned to wastes due to treatment of the generated pollution and included in the economic objective function. 3.4. Environmental Risk Implications for Maintenance. Having identified the most environmentally benign and economically optimal set of designs with respect to all sorts of release scenarios, the idea of criticality analysis (Thomaidis and Pistikopoulos, 1995) can be applied to identify and rate the most critical events with respect to plant performance and the environment. More specifically, we are interested in the sensitivity of environmental risk σ (t) to the probability of an event l, Fl*. Then,

k∈K

1

Pk(t)EIk dt e ∫ H∑ H k∈K

x ∈ X, y ∈ Y ∈ {0, 1}n The continuous variables x represent flows, operating conditions, and design variables. The binary variables y denote the potential existence of process unit blocks and optionally streams, interconnections. is a parameter vector that imposes stricter legislation on pollutant discharge. These variables typically appear linearly as they are included in the objective function to represent fixed charges in the purchase of process equipment (in the term cTy) and in the constraints to enforce logical conditions (By + Cx e d). The term F(x) denotes purchase costs for process equipment, raw material purchase costs, product/byproduct sales revenues, and utility costs. The sizing equations correspond to h(x) )

) ∂Fl*

∑

k∈K

{

k

NREI

}

∂Pk(t) ∂Fl*

(11)

since the estimation of NREIk is not influenced by Fl*. Note that (11) allows for equipment/events that can be ranked according to their corresponding criticality index as a function of time. While the mathematical details for the estimation of σ(t) in (11) are described elsewhere (Thomaidis and Pistikopoulos, 1995; Stefanis, 1996), the results from such a ranking can be used as guidelines for maintenance and environmental optimization based on quantitative information regarding maintenance resources (number of service crews, job durations, etc.) and tasks (equipment maintenance specifications, list of scheduled preventive maintenance activities). The designer can then explore opportunities for maintenance execution based on a formal assessment of the deterioration of the operating and, hence, environmental system performance over time and the relative effect of restoring the performance of critical equipment on the environmental damage caused by unintended emissions. These issues are shown in more detail in the next section. 4. Examples Two example problems are presented in this section to illustrate the basic features of the proposed methodology. First, the reaction-separation example, described in section 2, is revisited highlighting the analytical steps of the methodology. The second example involves the production of chloromethanes which are among the most hazardous chemicals encountered in the chemical industry. Apart from issues regarding equipment failures, undesired events such as leakages from flanges and errors in measurements are also taken into account and their effects on environmental risk are discussed. 4.1. Example 1: Reaction-Separation Example. Consider the example presented in Figure 2, allowing for the possibility that some equipment is subject to


Figure 4. Functional logic AND/OR digraph, example 1.

failures. The methodology for environmental risk assessment is then applied, as follows: (a) System Boundary and Emissions Inventory. Although this work focuses mainly on process environmental trade-offs, the global boundary around the process of interest can be defined to allow for inputoutput waste interactions based on the life cycle analysis principles. The process emissions inventory comprises chemicals A, B, C, and D that are regularly emitted through the purge stream but can also lead to a potential environmental threat depending on whether equipment fail. (b) Environmental Impact Assessment of Routine and Nonroutine Releases. Concentrating mainly on the point source environmental damage, the critical air mass (CTAM, tn air/h) is employed to assess both routine and nonroutine releases. The environmental impact vector of routine releases EIo is quantified as

FAo FBo FCo FDo EI ) CTAM ) + + + (12) SLVA SLVB SLVC SLVD o

o

where Fow denotes the mass flowrate of each emission w at the fully operable state o and SLVw its standard legislation value (kg w/kg air). In order to quantify the environmental impact of nonroutine releases and hence the environmental risk of the process, the algorithmic procedure proposed in section 3 is followed. In more detail, all operable degraded states must be identified. The nonoperable states at which the system resides are not taken into account since the plant is shut down, and therefore, no contribution to environmental damage is achieved. Having constructed the process reliability diagram (see Figure 4), the corresponding fault tree, presented in Figure 5, indicates that the operable system states to be considered in the risk assessment are the following: state, k

description

1 2 3 4 5 6

fully operable (o) reactor R-1 fails reactor R-2 fails recycle compressor CR-2 fails recycle compressor CR-2 and reactor R-1 fail recycle compressor CR-2 and reactor R-2 fail

Figure 5. Fault tree, example 1.

Failures regarding the inlet compressor, the liquid product pump, and the heat exchangers lead the system to nonoperable states and therefore are not included in the analysis. Given the equipment reliability data (Table 4) and considering a process horizon of 4 years, the system’s performance in terms of reliability as a function of time is illustrated in Figure 6; assuming statistically independent failures, according to step 2 of the proposed procedure, the cumulative probabilities of each discrete state are state, k

1

2

3

cum. probability

0.52

0.15

0.12

4

5

0.08

0.04

6 0.03


Figure 6. Overall system reliability, example 1.

Figure 8. Environmental risk (NREI) vs total expected environmental damage (CRNREI), example 1.

Figure 7. Pareto curve of optimal solutions, example 1.

Figure 9. Contribution of system states to total expected impact (CRNREI), example 1.

As expected, the fully operable state has the highest relative influence on the overall environmental damage; on the other hand, it is unlikely that both a reactor and the recycle compressor fail at the same time, indicating the low relative influence of this scenario. Design Optimization for Minimum Environmental Risk. Having assessed all sources of routine and nonroutine releases, problem (9) is then solved, so as to estimate the optimal process design (in this case the reactor volumes) and the operation of each state that will minimize routine and nonroutine environmental damage. Employing the algorithm proposed in Figure 18, the results can be summarized as follows: (a) There exist trade-offs between the expected environmental damage CRNREI that accounts for all degraded states including the fully operable and the associated expected cost expressed on an annualized basis. Figure 7 shows the family of optimal designs that form the pareto curve of optimal solutions between the expected values of annual cost and critical air mass. As can be seen, the minimum expected environmental damage, including routine and nonroutine process releases, is 16.5% lower compared to the environmental damage corresponding to the minimum cost release scenario solution! Note also that the cost penalty for such a pollution prevention strategy is only 7% higher than the minimum cost case.

(b) The environmental impact of nonroutine releases NREI, from eq 8, while having a significant contribution to the overall environmental degradation, does not always decrease as the overall expected environmental damage CRNREI decreases. Indeed, as depicted in Figure 8, there is a critical value of the combined impact above which the environmental risk of the process in fact increases! This is due to the fact that the relative contribution of the degraded states becomes less important to the overall environmental damage as the environmental constraints become tighter (see Figure 9). This is expected since tighter environmental regulations necessitate the selection of an economically beneficial design that will minimize the damage of the fully operable state even though it might result in an increase of the environmental risk related to the less possible degraded states. The design corresponding to this critical value (see Table 6), although not the cost or global environmental optimal solution as can be seen from the pareto curve in Figure 7, provides a very good compromise solution without entailing excessive cost. The detailed results for each state, summarized in Table 7, indicate that the case of simultaneous failure of CR-2 and R-2 generates the most waste damage, since in this case, a large amount of the most toxic chemical D is released to the


Figure 10. Optimal reactor volumes at the environmental/cost optimal pareto curve, example 1.

Figure 11. System response and maintenance policy for minimum environmental risk, example 1.

Table 6. Optimal Design for Minimum Environmental Risk annual cost (M$) risk, NREI (tn air/h) env. impact, CRNREI (tn air/h) vol of reactor 1 (L) vol of reactor 2 (L)

199.6 14 400 35 820 566 574.9

Table 7. Optimal Results of Degraded States state, k impact, EIk (tn air/h) risk, NREIk (tn air/h) annual cost, COSTk (M$) feed fraction of A (% mol) feed fraction to R-1 (% mol) input raw feed of D (kgmol/h)

1

2

3

4

5

6

23 050 27 911 62 853 73 169 73 169 133 305 0 4861 39 805 50 119 50 119 110 200 208.48 223.68 217.24 229.61 229.61 215.04 25

0

48

0

0

43

45

0

100

0

0

100

13.46

35.6

0

37.71

37.71

0

environment. For the same reason, state 3 causes very high damage compared to state 2. The inlet raw material flow rates displayed in the same table show how the operation changes for each state to reach minimum overall environmental damage. (c) The optimal volumes corresponding to the pareto space of solutions are shown in Figure 10. Note that the common kink in both curves corresponds to the design with minimum environmental risk. Furthermore, the fact that the volume of R-2 varies significantly (from 575 to 830 L) shows that its efficient design is crucial for environmental damage and cost minimization. This result is also verified by the criticality analysis reported below. Critical Equipment and Preventive Maintenance Policy. In order to establish an efficient maintenance policy that will prevent undesired equipment failures and decrease environmental risk, all events must be ranked with respect to their effect on the process environmental performance as analyzed in section 3.4. A (scaled) sensitivity index, 0 e σ e 1, at the beginning of the plant operation and after 1 year, is presented in Table 8. The results reveal that the recycle compressor CR-2 is the most critical equipment, followed by reactor R-2 and then by R-1. On the basis of the above results and considering that reactors R-1 and R-2 need to be maintained every 7000 and 6000 h, respectively, compressor CR-2 every 5000 h and specified a target for environmental risk NREIT

Figure 12. System response and maintenance policy for minimum cost, example 1. Table 8. Criticality Index of Equipment Failures for Example 1 equipment failure

σt)0

σt)1yr

compressor CR-2 reactor R-2 reactor R-1

1 0.8 0.1

1 0.7 0.01

of 2500 tn air/h, the preventive maintenance policy to be followed in the first 2 years is shown in Figure 11. The dynamic response of the system indicates that equipment is maintained as a result of (i) regularly planned maintenance tasks (for example, in the case of R-2 and R-3 at time t ) 6000 and 7000 h, respectively, see Figure 11) and (ii) increase of environmental risk NREI above the desired limits (such as tasks involving maintenance of CR-2). It is worth noting that, in the case of cost minimization, the corresponding maintenance schedule and the resulting system response are quite different (Figure 12). Although the number of maintenance tasks remains the same, their timing and sequence has changedsthere is a certain preventive maintenance policy that needs to be followed to keep the environmental damage of nonroutine releases low. 4.2. Example 2: Production of Chloromethanes. Consider the simplified chloromethane reaction sub-

3702 Ind. Eng. Chem. Res., Vol. 36, No. 9, 1997 Table 9. Reliability Data for Example 2 horizon, H ) 4 yr CR-1

event

system (Austin, 1984; Fusillo and Powers, 1988) shown in Figure 13. Chloromethanes are produced according to the following reaction scheme

β)1

MTTR ) 72 h

F O2 ) ERRCl2:CH4 ) ERRTREA ) +8% +5% 1 mm leak 3 mm leak 0.1 kgmol/h

λ (1/h)

Figure 13. Simplified chlorination flowsheet.

R ) 120 000 1/h

3 × 10-6

5 × 10-6

1 × 10-5

4 × 10-6

1 × 10-6

are adjusted so that the chlorine to methane molar ratio at the inlet of the reactor has a value of 1.3. Most of the process equipment is highly reliable apart from (i) the recycle compressor system, which has a performance described by a Weibull function, and (ii) the measuring devices monitoring the ratio of chlorine to methane fed to the reactor, the air feed flow, and the reaction temperature. The measurement errors are regarded as discrete events, and as their probability drifts with respect to time, they are described by an exponential density function of the following form:

CH4 + Cl2 f CH3Cl + HCl

(rxn 1)

f(t) ) λ exp(-λt)

CH3Cl + Cl2 f CH2Cl2 + HCl

(rxn 2)

CH2Cl2 + Cl2 f CHCl3 + HCl

(rxn 3)

CHCl3 + Cl2 f CCl4 + HCl

(rxn 4)

In addition, the exponential distribution model is used to describe the probability of occurrence of external events such as gaseous leaks from the recycle piping system that have occurred in the past. Table 9 summarizes the required reliability data for each event (Thomaidis, 1995). The following environmental data (Habersatter, 1991 and UK Ecolabelling Board, 1993) are also supplied for the process of interest:

that takes place in the gas phase with chlorine as the limiting reactant. The design must be such that chlorine is not allowed to accumulate in large quantities in the reaction system due to explosion hazards; therefore, it should not exceed a specified stoichiometric amount with respect to methane reactor feed. The system is equipped with vents to the atmosphere and the separation system (which is not included in this case for simplicity). There is an air feed line that is open when the system is not operating. Pressure effects are negligible, and the reactor operates at 3 atm. A twostage recycle compressor with intercooler is required, which is assumed to operate adiabatically, followed by a gas-fired heater to ensure that the inlet reactor gases are partially preheated by the recycle gases to reach a sufficiently high temperature to minimize heat control problems. While the kinetics of the reaction scheme are given in Appendix II, the following operating constraints need to be satisfied for inherently safe operation in order to produce a stream of 50 kgmol/h to be fed directly to the separation block:

400 e reactor temperature (°C) e 457 air feed ) 0 chlorine to methane molar feed ratio e 3 Temperatures much above 450 °C cannot be tolerated since pyrolysis would occur. Pyrolysis is a very exothermic reaction and once initiated quickly reaches explosive violence. The presence of oxygen in the system decreases the rate of the reaction (1.25% wt oxygen in the reactor feed decreases approximately by 50% the rate of chlorination at the studied temperature range), as it behaves as an inhibitor. High chlorine to methane molar feed ratios result in accumulation of large amounts of chlorine in the system that may lead to explosion; for this reason, material input flow rates

chemical Cl2 CH4 CH3Cl CH2Cl2 CHCl3 CCl4 HCl O2

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max acceptable concentrn (kg/tn air)

global warming potential (kg CO2/kg pol)

1.67 × 10-5 0.0125 8.333 × 10-6 8.333 × 10-6 8.333 × 10-6 8.333 × 10-6 8.333 × 10-5

11 5 15 25 1300

System Boundary and Emissions Inventory. The system boundary is considered around the methane chlorination process, and therefore, the emissions inventory consists mainly of chlorinated hydrocarbons, unreacted raw materials and byproducts vented to the atmosphere:

waste vector ) [Cl2 CH4 CH3Cl CH2Cl2 CHCl3 CCl4 HCl O2]process Environmental Impact Assessment of Routine and Nonroutine Releases. As in example 1, the waste vector defined above is aggregated into an environmental impact vector of low dimensionality, reflecting the actual damage caused to the environment. In this case, the metrics employed to investigate the routine/nonroutine environmental behaviour of the process are T EI ) [CTAM GWI]process

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and depend on the mass of pollutant discharged, the maximum acceptable concentration limits, and the global warming potentials defined by the user (see the table above). The process reliability diagram and the corresponding fault tree are similar to those shown in Figures 4 and

Ind. Eng. Chem. Res., Vol. 36, No. 9, 1997 3703 Table 10. System Degraded States for Example 2 state k 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31

ERRCl2:CH4 ERRTREA 1 mm 3 mm FO2 ) 0.1 CR-1 kgmol/h fails ) 8% ) 5% leak leak X X X X X X X X X X X

X X X X X X X X X

X X X X X X

X X X X X X

X X X

X X X X

X

X X X X X X X X

X X X X X X X X

X X

Table 11. Summary of Results for Example 2 min expected min min COST CRNREICTAMH CRNREIGWIH annual cost (M$) NREICTAMH (106 kg air/h) NREIGWIH (kg CO2/h) VR (m3)

Figure 14. Environmental risk response with respect to time, example 2.

X X

X X X X X X

X X X

195 225 2622 12878 2.44

253 540 2414 8612 2.6

209 670 2630 2445 2.49

5. The major differences are that (i) AND gates are reduced by one due to the fact that there is one reactor only and (ii) additional nodes to describe external events are attached to the recycle AND gate. The probability of the system degrading into a nonoperable state is negligible, since both mixers, inlet valves, and the reactor are fully reliable. The external events are all assumed to cause degradation to operable states with decreased reliability and, therefore, according to Table 10, the operable degraded system states number 31. The state probability estimation, according to eq 3 indicates that (i) a 1 mm leak on the recycle is more likely to occur than any other undesired event, (ii) all external events have greater probabilities of occurrence than failure of CR-1, and (iii) simultaneous occurrence of more than two undesired events is most rare. Design Optimization for Minimum Environmental Risk. The optimization problem is posed as explained in section 3.3; the design variable to be optimized is the volume of the reactor VR (1.5 e VR (m3) e 3), and the degrees of freedom for each operable state are listed below:

675 e nominal reactor temperature (K) e 730 0.2 e recycle to separations molar ratio e 0.971 900 e heater outlet temperature (K) e 1200 The results summarized in Table 11 reveal some interesting points:

(a) Cost optimization yields a smaller reactor (2.44 m3) but at the same time results in substantially increased global warming impact due to nonroutine releases. (b) By minimizing the expected value of critical air mass, a 8% reduction of environmental risk NREICTAM can be achieved compared to the corresponding cost optimal value (see Table 11). In addition, environmental risk related to global warming is reduced by almost 33%. However, one has to pay an economic penalty for pollution reduction in this case, as optimization of CRNREICTAM has a negative effect on the economics of the process (30% increase in cost). (c) Optimization of CRNREIGWI yields the most interesting results since the contribution of nonroutine releases with respect to global warming is reduced by 6-folds! At the same time, the annual cost and the critical air mass are maintained at low levels and the optimal reactor design is quite similar to its cost optimal. (d) The dynamic response of environmental risk NREI(t), corresponding to the cost optimal case, is presented in Figure 14 and shows that both GWI and CTAM risks increase with respect to time, as the reliability of the system decays. Note that both environmental metrics are based on steady-state environmental behavior of pollutants and in the context of this work the time dependence is a result of the reliability analysis. The time averaged integral of the dynamic response results in the risk values presented in Table 11. (e) Figures 15 and 16 demonstrate the deviation of the environmental impact metrics CTAM and GWI, respectively, from their fully operable state values for each of the 31 degraded states. As can be observed from both graphs, failure of CR-1 (states 7, 12, 16, 19, 23, 29, and 31) results in significantly increased damage in every case. The following trends can also be revealed concerning CTAM (see Figure 15): (i) the air pollution damage that corresponds to optimization of CRNREICTAMH is consistently less for each state apart from state 2 (measurement error in molar feed ratio of reactants), verifying the fact that total CTAM is optimal in this case, and (ii) minimization of CRNREIGWIH results in larger CTAM in states above k ) 22; the overall CTAM though does not increase significantly because of their low probability of occurrence. Figure 16 shows that


Figure 15. CTAM deviation from fully operable case for each degraded state, example 2.

Figure 16. GWI deviation from fully operable case for each degraded state, example 2.

GWI deviation is less in almost every state in the case of CRNREIGWIH minimization but is significantly greater when expected CTAM is minimized (see states 10, 27, and 28). Therefore, the solution corresponding to global warming optimization seems to be the better compromise with respect to both cost and critical air mass.

Critical Equipment and Preventive Maintenance Policy. In order to detect the process bottlenecks with respect to environmental risk, a criticality analysis is performed with respect to the environmental impact vector of nonroutine releases, NREI. The scaled criticality index σ, presented in Table 12, demonstrates


Figure 17. NREIGWI response and maintenance policy for minimum global warming, example 2. Table 12. Criticality Index of Equipment Failures for Example 2 event

σt)0

σt)1yr

CR-1 fails 3 mm leak 1 mm leak ERRCl2:CH4 ) +8% FO2 ) 0.1 kgmol/h ERRTREA ) +5%

1 0.001 0.001 0.001 0.001 0.001

1 0.076 0.072 0.001 0.001 0.001

that failure of the recycle compressor is the main bottleneck of the process, as it has the largest effect on environmental damage, followed by the leaks on the recycle and finally the measurement errors. The preventive maintenance policy obtained to satisfy NREIGWI(t) e 1000 kg of CO2 is presented in Figure 17. The equipment maintenance policy dictates that CR-1 must be maintained every 5000 h of operation. 5. Concluding Remarks Initial developments toward a formal theory and a process systems methodology for quantifying and minimizing the adverse environmental effects of routine and nonroutine releases has been presented in this paper. The proposed framework identifies not only routine but also nonroutine waste generation related to accidents, process deviations, and other undesired events that may occur during the life cycle of an industrial process. In order to quantify the environmental impact of nonroutine releases, in addition to conventional environmental impact assessment, a quantitative risk analysis step is developed based on formal reliability assessment techniques, accounting for possible release scenarios for various types of nonroutine pollution related to internal events (such as releases due to equipment failure) or external events (such as fugitive emissions due to small leaks or spills from pumps or flanges). This information is then used to evaluate the environmental impact index (EI) of the fully operable state and the vector of nonroutine release environmental impact (NREI), defined as the weighted sum of deviations from the standard release scenario, indicating the environmental risk of the process. On the basis of detailed models to describe the design and operational characteristics of a given process (i.e., reliability, maintenance, and environmental models), an optimization problem is formulated and solved parametrically to detect the optimal

Figure 18. Algorithm for design optimization.

operation of each degraded operating state and the optimal process design that is economically acceptable and at the same time features minimum environmental risk. A (scaled) environmental impact-criticality index, σ, is also defined to assist identify process bottlenecks and opportunities for preventive maintenance. Current research efforts are focusing on more involved safety considerations, the impact of control action, and controller design and condition-based maintenance optimization (see, for example, Moon et al., 1992). Links to pollution preention synthesis/design methodologies will also be established (see, for example, El-Halwagi et al. (1996) and Papalexandri and Pistikopoulos (1996)). Nomenclature CRNREI ) combined vector of routine and nonroutine environmental impact CTAM ) critical air mass metric CTWM ) critical water mass metric d ) design variables EI ) environmental impact vector F(x) ) cost function f(t) ) failure density F ) material flowrate g(x) ) design specifications GWI ) global warming impact H ) time horizon h(x) ) sizing equations j ) equipment index k ) state space K ) set of operating states L ) Lagrangian function

3706 Ind. Eng. Chem. Res., Vol. 36, No. 9, 1997 l ) event index MTTR ) mean time to repair NREI ) vector of nonroutine environmental impact POI ) photochemical oxidation impact Pk ) probability that the system resides over state k Qj ) equipment unavailability Rj ) equipment reliability Sk, (Sk) ) index set for operational (failed) components of the equipment in state k SMD ) solid mass disposal SODI ) stratospheric ozone depletion impact SLV ) standard legislation value t ) time x ) continuous variables y ) 0-1 integer variables w ) waste index description

decisions, equipment volumes) and a set of noncomplicated, operating variables x (such as flows, temperatures, etc). For a given design d, the continuous operating variables for each state xk are optimized in the NLP primal subproblem, which has the following form

min Fk(d,xk) s.t.

hk(d h ,xk) ) 0 gk(d h ,xk) e 0

Greek Symbols ) pareto parameter λ ) failure rate λk ) Lagrange multiplier µk ) Lagrange multiplier F ) probability of failure σ ) criticality index with respect to environmental risk description

CRNREIk(d h ,xk) )

min F(x,y) x,y

g(x) e 0 NREI(x,y) )

1

∫H ∑ Pk(t)(EIk - EIo) dt

H

CRNREI(x,y) )

k∈K

1

∫ ∑ Pk(t)EIk dt e H H k∈K

x ∈ X, y ∈ Y ∈ {0, 1}n is obtained, assuming (a) steady-state process and environmental (considering either point source or pollutant fate behavior) models are used, (b) individual components reside in either an operable or failed state, (c) all events are statistically independent, (d) reliability data are available as functions of time for equipment failures and all external events. However, since most chemical operating systems are subject to a variety of failures and external disturbances, the operable system degraded states can be numerous. To overcome the above difficulty, an iterative procedure is proposed, based on a modified generalized benders decomposition (Geoffrion, 1974) scheme, as can be seen in Figure 18. The problem is decomposed into an NLP primal subproblem and an (MI)LP master subproblem. (It should be noted that no structural alternatives are considered in the context of this work; however, the proposed algorithm can handle structural decisions without any modification.) The set of continous variables x is divided into a subset of design variables that together with the structural variables comprise the set of complicated variables d (structural

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d

s.t.

L (d) )

h(x) ) 0

∫HPk(t) dt

min a

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s.t.

EIk H

The solution of the k above subproblems provides an upper bound to the final solution, whereas the design variables are optimized in the master problem, which corresponds to the Lagrangian dual problem of the original (MI)NLP and provides a lower bound to the (MI)NLP solution

Appendix I: Algorithm for Design Optimization The solution of the resulting multiobjective optimization problem,

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xk

∑k Fk(d,xjk) + λk hk(d,xjk) + µk gk(d,xjk) T

T

a g L (d) By using parametric optimization techniques (Acevedo and Pistikopoulos, 1996), the pareto curve of optimal solutions between cost and CRNREI can be generated. Appendix II: Methane Chlorination Kinetics For a completely backmixed reactor, the following series of equations is used to predict the single-pass product distribution curves (McKetta, 1991)

φ C3o C3 1 - φ C2o ) C2o K2 φ + 1 - φ K1 φ+

K2 C3 φ C4o + K1 C2o 1 - φ C2o C4 ) C2o K3 φ + 1 - φ K1 K3 C4 φ C5o + C5 K1 C2o 1 - φ C2o ) C2o K4 φ + 1 - φ K1 K4 C5 C6 K1 C2o C6o + ) C2o φ C2o 1-φ

(AII.1)


where subscripts 1, 2, 3, 4, 5, and 6 refer to chlorine, methane, methyl chloride, methylene chloride, chloroform, and carbon tetrachloride, respectively, subscript “o” refers to the initial concentration, C represents the molar concentration of each component, φ is defined as

φ)

C2 C2o

and Krxn (1/s) are the reaction constants that vary with temperature as follows (McKetta, 1991).

log Krxn1 ) 6.145 × 10-3(T(F)) - 4.935

(AII.2)

log Krxn2 ) 6.101 × 10-3(T(F)) - 4.499 log Krxn3 ) 6.127 × 10-3(T(F)) - 4.77 log Krxn4 ) 6.103 × 10-3(T(F)) - 5.31 Literature Cited Acevedo, J.; Pistikopoulos, E. N. A Parametric MINLP Approach for Process Synthesis Problems under Uncertainty. Ind. Eng. Chem. Res. 1996, 35, 147-158. Aelion, V.; Castells, F.; Veroutis, A. Life Cycle Inventory Analysis of Chemical Processes. Environ. Prog. 1995, 14(3), 193-200. Austin, G. T. Shreve’s Chemical Process Industries, 5th ed.; McGraw Hill: New York, 1984. Christou, M. D. Environmental Risk Assessment and Management: Towards an Integrated Approach. Proceedings of Probabilistic Safety Assessment and Management ‘96-Crete, Greece; Springer-Verlag: London, 1996; Vol. 2, pp 700-704. El-Halwagi, M. M.; Hamad, A. A.; Garrison, G. W. Synthesis of Waste Interception and Allocation Networks. AIChE J. 1996, 42(11), 3087-3101. Fusillo, R. H.; Powers, G. J. Computer-Aided Planning of Purge Operations. AIChE J. 1988, 34(4), 558-566. Geoffrian, A. M. Generalized Benders Decomposition. J. Optim. Theory Appl. 1974, 10, 237-260. Habersatter, K. BUWAL Report:Ecobalance of Packaging Materials State of 1990, 1st ed.; F.O.E.F.L.: Zurich, 1991. HMSO. A guide to Risk Assessment and Risk Management for Environmental Protection; Department of the Environment: London, U.K., 1995.

Hwang, G. L.; Masud, A. S. Multiple Objective Decision MakingsMethods and Applications; Springer: Berlin, 1979. Lapp, S. A. A Risk Evaluation System. ChemTech 1991, 700-704. McKetta, J. J. Chemical Engineering Design Encyclopedia, 1st ed.; Marcel Dekker: New York, 1991. Montague, D. F. Process Risk EvaluationsWhat method to use? Reliab. Eng. Syst. Saf. 1990, 29, 27-53. Moon, I.; Powers, G. J.; Burgh, J. R.; Clarke, E. N. Automatic Verification of Sequential Control Systems using Temporal Logic. AIChE J. 1992, 38(1), 67-75. Papalexandri, K. P.; Pistikopoulos, E. N. General Modular Representation Framework for Process Synthesis. AIChE J. 1996, 42(4), 1010-1032. Pistikopoulos, E. N.; Thomaidis, T. V. Towards a Design Framework for Flexibility, Reliability and Maintenance; AIChE Annual Meeting, Miami Beach, FL, 1992; Paper 140b. Pistikopoulos, E. N.; Stefanis, S. K.; Livingston, A. G. A Methodology for Minimum Environmental Impact Analysis. AIChE Symp. Ser., Volume on Pollution Prevention through Process and Product Modifications 1995, 303, 139-151. Sarigiannis, D. A.; Volta, G. Ecological Vulnerability Analysis: Towards a New Paradigm for Industrial Development. Proceedings of Probabilistic Safety Assessment and Management ‘96sCrete, Greece; Springer-Verlag: London, 1996; Vol. 1, pp 478-484. Seveso Directive. Chem. Br. 1996, 32(10), 15-20. Stefanis, S. K. A Process Systems Methodology for Environmental Impact Minimization. Ph.D. Thesis, Imperial College, 1996. Stefanis, S. K.; Livingston, A. G.; Pistikopoulos, E. N. Minimizing the Environmental Impact of Process Plants. Comput. Chem. Eng. 1995, 19S, S39-S44. Stefanis, S. K.; Livingston, A. g.; Pistikopoulos, E. N. Environmental Impact Considerations in the Optimal Design and Scheduling of Batch Processes. Comput. Chem. Eng. 1996, in press. Thomaidis, T. V. Incorporation of Flexibility, Reliability, Availability, Maintenance and Safety in Process Operations and Design. Ph.D. Thesis, Imperial College, 1995. Thomaidis, T. V.; Pistikopoulos, E. N. Optimal Design of Reliable and Flexible Process Systems. IEEE Transaction Reliab. 1995, 44(2) 243-250. UK Ecolabelling Board. Criteria for Hairspray Ecolabels, 1993.

Received for review December 10, 1996 Revised manuscript received April 11, 1997 Accepted April 12, 1997X IE9607816 X Abstract published in Advance ACS Abstracts, June 1, 1997.

Methodology for Environmental Risk Assessment of Industrial

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