Xi

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[Flowsheet Decomposition!

Calculate Indicators

3.1 MasslEnergy indicators

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32 Su 5 taxability Metrics 3.3 Safety Indices

Calculate Indicators

3.4 Operational Indicators

3.5 Compound indicators kL.

STEP4

p.mmn.nm

f Indicator Sensitivity Analysis]

I i

Target Indicators

Target Variable J

-----1

Sensitivity analysis operational parameters

Sensitivity analysis operational parameters

Generate.'Evaluate new design alternatives

' ContinuousJBatch Processes

Batch Processes M Tools ----Data-Flow

Performance Analysis pro || £23 Aspen Tech [f^J ICAS-WAR algorithm

Super Pro Designer ICAS11 M CAPEC Database ^ ICAS - ProCamd Bpqt?.r,| Gproms ¿4] HYSYS Sa ICAS-ProPred ^ ICAS-Thermodynamic insights

Figure 5.1 Activity-flow of the indicator-based methodology.

in the system can be due to its entrance through a feed stream or by its production in a reactor unit. The exit of the respective compound can be due to a 'demand' (exit) stream or by its reaction in a reactor unit. Figure 5.2 provides an illustration of the open- and closed-paths.

In Figure 5.2 one open-path, which includes streams {1,2,3,4,5} and a closed-path involving streams {2,3,4,6} are highlighted. More details can be found in [10],

->• Process stream ■> Closed-Path ■> Open-Path

Figure 5.2 Example of closed- and open-paths.

where the algorithms for flowsheet decomposition to identify the open- and closed-paths are explained.

5.3.1.3 Step 3: Calculation of Indicators

In this step the mass/energy indicators, the safety index and the sustainability metrics are calculated.

Calculation of Mass and Energy Indicators There are five mass indicators and three energy indicators. A brief description of each of these indicators is given below. The equations to calculate the indicators can be found in [10].

Material Value Added (MVA) This indicator gives the value added between the entrance and the exit of all compounds present in the system. In other words, this means the value generated between the start and the end point of the path. Consequently, this indicator is only applied to open-paths. To calculate this indicator it is necessary to know the purchase price or the costs related to the production of a given compound as well as its sale price. This indicator is calculated as the difference between the sale price and the purchase price of the corresponding chemical in each open-path. Negative values of this indicator indicate that the compound has lost its value in this open-path and consequently point to potentials for improvements. Positive values of this indicator show that the compound has win value through the process and consequently that path is a benefit for the process. The indicator value is given in terms of monetary units per year, which indicates the money lost across that open-path. Two examples are given to assist understanding:

1) MVA is negative for a raw material: This means that a raw material looses its value as it passes through the unit operations in the corresponding open-path and exits the process. That is, the chemical (raw material) at exit has a lower value corresponding to its value at the entrance of the process. This will point to design alternatives, such as recycling of the raw material, improvements in separation processes, improvements in the reaction conditions, etc., that will make the MVA less negative for the targeted raw material.

2) MVA is negative for the main product: This means that the main product is being wasted in the corresponding open-path and the separation unit responsible for its recovery needs to be improved in order to improve the value of this product while reducing the waste.

The selected MVA indicator is the one having the biggest monetary loss and consequently is the one that should be selected in a further methodology step.

Energy and Waste Cost (EWC) This indicator is applied to both open- and closed-paths. The value of EWC represents the maximum theoretical amount of energy that can be saved in each path within the process and consequently this value is always positive. High values of this indicator point to high consumption of energy and consequently changes in the associated paths should be considered in order to reduce this indicator value. This can be done in two ways, reducing the path flowrate or changing the conditions in order to decrease the heat duties. The EWC value is given in terms of monetary units per year.

Two examples are given to assist understanding:

1) EWC is high for a solvent (a chemical found in the system) in a closed-path: This means that this solvent in a recycle-loop is consuming a lot of energy. The unit operation within the closed-path might be replaced in order to optimize the heat transfer and consequently reduced the energy consumption. Another option is to reduce the solvent flowrate by selecting a new solvent, if it does not compromise the separation efficiency.

2) EWC is high for an inert compound in an open-path: This means that the inert compound is entering into the process and consuming a lot of energy. This might be reduced by inserting a separation unit to remove the inert compound before it enters the process. The other option is to improve the post-reaction separations related to the removal of the inert compound.

The selected EWC is the one which has the highest value because it then indicates the biggest monetary loss and consequently it should be selected as the target for further improvement.

Reaction Quality (RQ) This indicator measures the influence ofa given path with respect to process productivity. This indicator is applied to both closed- and open-paths. Positive values of RQ indicate a benefit of this path with respect to process productivity, while, negative values point to a decrease in the process productivity. This indicator helps in the selection of MVA and EWC indicators. If a path shows a very negative MVA or a high EWC, RQ is used to check if that path is a benefit for the process (positive values) or a bottleneck (negative values).

Two examples are given to assist understanding:

1) EWC value is high for a raw material in a closed-path and RQ positive: This means that a raw material in a recycle is consuming a lot of energy even though RQ is positive. Consequently, another EWC should be considered as target for improvement.

2) EWC value is high for one of the products in a closed-path and RQ negative: This means that the corresponding product in a recycle is consuming a lot of energy. RQ is negative, which indicates that the recycling of the product is not advantageous for the process. Consequently, EWC should be considered as target for improvement.

The RQ indicators having negative values should be selected for further investigation.

Accumulation Factor (AF) This indicator determines the accumulative behavior of the compounds in the closed- paths. This corresponds to the amount that is recycled relative to the input to the process and not the inventory. This indicator can only have positive values. High values of this indicator point to high potentials for improvements, due to the high accumulation.

Two examples are given to assist understanding:

1) AF is high for a solvent in a closed-path: This means that the solvent has a large accumulation within in the system. If the analysis is being done for a new process, it indicates that the selection of another solvent which requires a lower flowrate and consequently smaller equipments could be a good option.

2) AF is high for a raw material and for by-products in a closed-path: This means that the raw material has a large accumulation and consequently, the byproducts might be responsible for the raw material accumulation. Therefore, these by-products should be removed from the recycle by, for example, improving the separations, inserting new separations, or, improving the reaction conditions.

The AF indicator having high values should be selected for further investigation.

Total Value Added (TVA) This indicator describes the economic influence of a compound in a given path. TVA joins two of the previous indicators EWC and MVA and the following equation is used to calculate its value:

Negative values of this indicator point to high potential for improvements in terms of decrease in the variable costs. The values of this indicator should be analyzed carefully, because MVA can have a high positive value and consequently hide the problems in EWC. Therefore, even if the TVA value does not present a very negative value, the values of EWC and MVA should be analyzed separately in order to confirm that they really do not show any problem.

Energy Accumulation Factor (EAF) This indicator determines the accumulative behavior of energy in the closed-paths. This means, that high recycles of energy correspond to high energy integration. Low values of this indicator highlight the potential for saving energy consumption in the system using heat integration.

One example is given to assist understanding:

EAF is low for an energy closed-path: This means that a lot of energy is being wasted and consequently heat integration might be considered inside the loop in order to reduce the energy consumption.

The EAF indicators having low values should be selected for further investigation.

Total Demand Cost ( TDC ) This indicator is applied only to open-paths and traces the energy flows across the process. For each energy demand in the process, the sum of all demand costs, which pass through it, are calculated. High values of this indicator identify the demands that consume the largest values of energy, so these are the process parts, which are more adapted to heat integration. One example is given to assist understanding:

TDC is high for an energy open-path: This means that a lot of energy is being released in that stream/unit and consequently units using heating systems might be integrated within this open-path to reduce the energy consumption.

The indicators, presenting high values of TDC, should be selected in a further methodology step.

Calculation of Safety Indices The safety of the process is another important parameter that should be taken into account. In this methodology, the inherent safety index, developed by Heikkila [8]. has been implemented. In order to determine the inherent safety index, values for a set of sub- i ndices need to be calculated. These sub-indices can be divided into two groups:

1) Chemical inherent safety: Heat ofthe main reaction, heat ofthe side reaction, chemical interactions, flammability, explosiveness, toxicity, corrosivity.

2) Process inherent safety: Inventory, process temperature, process pressure, process layout.

Heikkila [8] defined a scale of scores for each sub-index. These scales are based on the values of safety parameters, such as the explosiveness, the toxicity, the pressure of the process and so on. The sum of all the sub- i ndex scores is the Inherent Safety Index value; this parameter has the maximum value of 53. Note that the higher the value of this Inherent Safety Index, the more unsafe is the process. Therefore, the aim in all the design alternatives is to try to reduce the value of this index, if possible.

Calculation of Sustainability Metrics In this methodology, the sustainability metrics defined by the Institution of Chemical Engineers (see [7]) have been used. Azapagic has defined 49 metrics divided into three main areas: environmental, social and economic. Out of the 49 defined metrics, the methodology uses 23 of them. The use of the sustainability metrics follows the simple rule that the lower the value of the metric the more sustainable is the process. A lower value of the metric indicates that either the impact of the process is less or the output of the process is more.

The metrics take into consideration energy consumption, material consumption or water consumption per kg of product or per value added, economic factors such as profit, etc.

For the metrics related to environmental impact , instead of using the definition of Azapagic et al. [7] the definition proposed by Cabezas [11] has been used. Cabezas et al. proposed the waste reduction algorithm (WAR) in order to calculate the environmental impacts from a chemical process. This algorithm has been implemented as part of the indicator based methodology. To calculate these metrics, the flowrates for each compound coming into the process and leaving the process are needed as known information, that is, the steady-state process stream (simulation) data is needed.

Summarizing, the indicators are applied to the entire set of open- and closed -paths. With their values the critical points of the process and the areas that should be improved in the process are determined. The sustainability metrics and the safety index are calculated using the steady- state data for the global process and they are used to measure the impact of the process in its surroundings. They will be used as performance criteria in the evaluation of the new suggested design alternatives.

5.3.1.4 Step 4: Indicator Sensitivity Analysis Algorithm

In this step the target indicators are determined using the indicator sensitivity analysis (ISA) algorithm (see - 10]). To apply this algorithm the indicators having the highest potential for improvements are identified first. Then an objective function such as the gross profit or the process total cost is specified. A sensitivity analysis is then performed to determine the indicators that allow the largest positive (for profit) or negative (for cost) change in the objective function. The most sensitive indicators are selected as targets for improvements.

5.3.1.5 Step 5: Sensitivity Analysis of Operational Parameters

With the target indicators and their variables identified in Step 4 (Section 5.3.1.4) the next task is to determine the process-operational variables that cause the biggest changes in the target indicators for smallest changes in their values. This analysis is done by checking the influence of increments of 5, 10 and 15% in all the operational variables that influence the selected target indicator and the consequent effect in the target. The analysis is done using the following equation (OPV is the operational value):

AOPV = OPVfinal - OPVinitial (5 2)

Through this analysis, it is possible to determine the highest improvement in the indicator value. This value corresponds to the maximum theoretical of improvement that can be achieved in the target indicators. The results determine the operational variables that can cause the highest improvements in the process and consequently the variables that must be targeted to generate more sustainable design alternatives.

5.3.1.6 Step 6: Generation of New Sustainable Design Alternatives

The steps involved in the generation of sustainable design alternatives are highlighted in Figure 5.3. The flow diagram in Figure 5.3 shows four categories where the operational variables are involved.

Figure 5.3 Work-flow for generation of sustainable alternatives (Step 6).

• Categoryl: Operationalvariablesassociatedwithaseparation;

• Category 2: Operational variables associated with flowrate reduction in a closed- path;

• Category 3: Operational variables associated with a reaction;

• Category 4: Operational variables associated with flowrate reduction in an open - path.

Once the categories have been identified the corresponding synthesis algorithm is employed to generate more sustainable alternatives. The following synthesis algorithms are recommended for specific targeted problems.

• Separationsynthesis: ApplythealgorithmofJaksland etal. [12];

• Improvement in a separation unit: Apply the driving force based reverse design algorithm of D'Anterroches and Gani [13];

• Improvement in a reactive unit: Apply the attainable region based reverse design algorithm of D'Anterroches and Gani [13];

• Selection/substitution of solvents: Apply the algorithm of Harper [14];

The proposed new alternatives are simulated using the new flowsheet configuration or the new operational conditions. With this new data, the performance criteria are calculated again and a comparison between the new alternatives is done taking into account the following criteria. 'An alternative is considered more sustainable if and only if it improves the indicator targets without compromising the performance criteria'. From the proposed alternatives the one with the better results will be the one selected.

Methodology -Batch Mode

5.3.2.1 Step 1: Data Collection

For the batch case, data on the time of each operation, the equipment volume, the initial and the final mass for each compound in each operation, the mass entering and leaving each batch operation during the operation time and the energy used in each step are required. The purchase and sale prices for each chemical are also needed. All these data can be collected from the real plant and/or generated through model - based simulations.

5.3.2.2 Step 1A: Transform Equipment Flowsheet into an Operational Flow Diagram

For continuous processes the flowsheet diagram is a sequence of different equipments where in each equipment, a specific operation takes place. When the process is operating in the batch mode, the individual equipments may present a sequence of operations. In this methodology the batch process will be treated as a 'continuous' process in terms of the material and energy (data) flow from operation to operation (instead of equipment to equipment). Thus, the equipment-based flowsheet is transformed to an operational flow diagram (illustrated in Figure 5.4).

Equipment Flowsheet Operation Flowdiagram

Operation 1: Charge Operation 2: Mix Operation 3: Reaction Operation 4: Discharge Operation 5: Clean

Figure 5.4 Example for the transformation of equipment flowsheet in an operational flow diagram.

5.3.2.3 Step 2: Flow Diagram Decomposition

For a batch process, it is possible to identify all the open- and closed-paths for each compound as in the continuous mode once the operation flow diagram has been generated. However, for a batch operation flow diagram, a new path related to the accumulation of mass and energy is introduced. This new path is called accumulation-path (AP) and corresponds to the accumulation in a given operation. This path represents an average of the mass for each compound during the operation time. More details are given by [15].

5.3.2.4 Step 3: Calculation oflndicators

For batch processes the indicators presented in Section 5.3.1.3 are also applicable. For each type of batch operation, two new indicators are proposed: operation indicator (compares the performance of the operation) and the compound indicator (indicates for each operation, the compound most likely to cause operational problems). These new indicators provide important information about the batch processes in terms of which operation of a process flowsheet has comparatively more potential for improvements than the others.

Operation Indicators There are three operation indicators, the total free volume factor (TFVF) , the operation time factor (OTF) and the operation energy factor (OEF). With these indicators it is possible to have an analysis of the performance of the batch operations in terms of time, volume and energy. In the text below, the operational (batch) indicators are explained in more details.

Total Free Volume Factor (TFVF) This indicator gives the percentage of free volume compared to the total volume of the equipment.

TFVF=

TFVF=

In Eq. (5.3), Vq is the equipment volume in operation j, pc is the density of compound c, C is the total number of compounds present in operation j.

High values of this indicator indicate that the equipment volume is not filled to a high level and consequently points to a potential for improvements. Knowing where the equipment is not being fully occupied, there is a good chance of changing the material disposition among the operations in order to improve the performance of the sequence of operations. The indicator value is given as a fraction.

Operation Time Factor (OTF) This indicator points to the fraction of time that a given operation spends compared to the total time taken by the whole sequence of operations.

High values of this indicator show that a given operation is taking too much time and consequently this operation can be seen as the bottleneck in the operations flow diagram. This is also the limiting operation with respect to time. This indicator value is given as a fraction and it should be reduced in order to improve the process.

Operation Energy Factor (OEF) This indicator gives the percentage of energy used in a given operation compared to the total amount of energy consumed.

Where Ej is the energy consumed in operation j.

High values of this indicator point to an operation consuming too much energy when compared to others. This indicator also helps to identify opportunities for heat integration and to trace the heat integration possibilities among different equipments/operations. This indicator should be reduced to improve the process and its value is given as a fraction.

Compound Indicators A set ofcompound indicators, which allow the identification of the compound causing a bottleneck in a given operation, have been developed. There are three different compound indicators the free volume factor (FVF), the time factor (TF) and the energy factor (EF). The TF and the EF are applied for ti

OTFj =J-IE

each accumulation-path and their calculations are dependent on the type of operation, such as, mixing, reacting and separating operations.

High values of these indicators point to the compounds responsible for the identified problems in the operations. These values should be reduced.

Three examples are given to assist understanding:

1) TFVF is high for a given operation: This means that the equipment is not fully occupied and consequently a change in the material allocation among the operations can be made. Also, for a new plant, the size of the equipment, which is going to be acquired, could be made smaller than what was originally designed.

2) OTF is high for a separation operation: This means that this separation operation has a bottleneck with respect to time. TF has been calculated for each compound. Supposing that recovering compound A is the object of this separation and that TF has the highest value for compound B. This means that compound B is the responsible for the slow separation. This indicates the need for an improvement in the current separation or to insert a new one that makes the separation of A from B easier.

3) OEF is high for a reactor: This means that this operation is consuming a lot of energy. This points to investigation with respect to heat integration, between this operation and some other operation/unit. Supposing that EF has been calculated to all the compounds in the reactor and assuming that the inert compound has the highest EF and, it means that this compound is the one responsible for the high energy consumption in the reactor and options to remove it before the separation process should be investigated.

5.3.2.5 Step 4: Indicator Sensitivity Analysis Algorithm

In this step the target indicators are determined using the ISA algorithm (see Section 5.3.1.4). To apply this algorithm the indicators having the highest potential for improvements are identified first. Then an objective function such as the gross profit or the process total cost is specified. A sensitivity analysis is then performed to determine the indicators that allow the largest positive (for profit) or negative (for cost) change in the objective function. The most sensitive indicators are selected as targets for improvements.

5.3.2.6 Step 5: Sensitivity Analysis of Operational Parameters

A sensitivity analysis with respect to the operational (parameters) variables, which influence the target indicators, is performed. The analysis identifies the operational variables that need to be changed to improve the process in the desired direction (See Section 5.3.1.5).

5.3.2.7 Step 6: Generation ofNew Sustainable Design Alternatives

Synthesis algorithms are applied to generate new design alternatives (See Section 5.3.1.6).

SustainPro Software

Introduction

Based on the above methodology, a software product (SustainPro) has been developed to allow easy application of the methodology to generate more sustainable design alternatives in batch and continuous processes. SustainPro is an Excel based software, divided into 21 different Excel sheets, where two of the Excel sheets are Principal Menus, one with options for importing and exporting data and another to guide the user through the methodology steps. The remaining Excel sheets represent the different steps and sub-steps of the methodology presented in Figure 5.1. The inputs for SustainPro are the mass and the energy balance data as well as the prices of the compounds present in the process. SustainPro follows all the steps of the methodology, allowing thereby the creation/evaluation of a new alternatives strategy to any chemical process.

SustainPro Architecture

SustainPro architecture is highlighted in Figure 5.5. The main interface of SustainPro is divided into three parts:

Part I-Indicator analysis; Part II-Evaluation;

Part III-Generation and comparison of new alternatives.

To solve a sustainable design problem the user should perform Parts I, II and III sequentially. The built-in color code system guides the user through the different steps of the work-flow (see Figure 5.1). The user must follow the button highlighted in orange, which is the next step to be followed. The light blue button represents the steps already performed and the dark blue buttons indicate the steps that have not yet been performed.

Supporting Tools

Some supporting tools are used by SustainPro in each part of the analysis-a summary of the supporting tools is given in Table 5.1.

Figure 5.5 SustainPro architecture. 5.5

Case Studies

Figure 5.5 SustainPro architecture. 5.5

Case Studies

Continuous Processes: Biodiesel Production

A simplified version of the flowsheet for biodiesel production is represented in Figure 5.6. The feedstock (Jatropha oil) is first heated before entering the reactor. The unreacted methanol is recovered with a distillation. A liquid-liquid extractor is used in order to separate the glycerol (heavy phase) from the biodiesel (light phase). To finish, both of those products are purified in distillation columns. There is no oil recovery, nor has energy integration been made.

The methodology and SustainPro have been applied to this case study.

5.5.1.1 Step 1: Collect the Steady-state Data

The required detailed process data for the biodiesel production plant was taken from a simulation of the process in PRO/II.

Table 5.1 Summary of the supporting tools used by SustainPro.

Tools

Purpose

Interaction with SustainPro

Simulators

Generate mass and energy balances

Process Design ^

Process

Simulator

Specification w

Compound

M CAPEC Database

Mass and Energy

SustainPro (Step 1)

CAPEC database Compound properties

ProPred

Property prediction

Compound

M CAPEC Database

Compound Properties

SustainPro (Step 3)

Compound

CAPEC Database

Compound Properties

CAPEC Database

SustainPro (Step 3)

SustainPro (Step 3)

Estimated Compound Properties

PA-WAR algorithm

Environmental parameters

WAR algorithm parameters w

SustainPro (Step 3)

ProCamd

Solvent selection

WAR algorithm parameters w

SustainPro (Step 3)

SustainPro (Step 5)

Operational Conditions w

coud ProCamd

Solvent Suggestion w

SustainPro (Step 6)

CAPSS

Separation technique

SustainPro (Step 5)

Operational

Separation

selection

Conditions |

CAPSS

Suggestion w

SustainPro (Step 6)

5.5.1.2 Step 2: Flowsheet Decomposition

For this case study, the flowsheet decomposition generated 7 closed-paths, 34 open-paths.

5.5.1.3 Step 3: Calculate the Indicators, the Sustainability and the Safety Metrics

For the entire set of flow-paths, the full-set of indicators was calculated. The most sensitive mass indicators were identified and they are listed for open- and closed-paths in Tables 5.2 and 5.3.

For this case study the most sensitive indicators are the MVA-material value added, for the open-paths OP28, OP12 and OP2. They have very negative values, which means that a lot of money is wasted from the time the materials (compounds) enter the system to the time they exit. OP 16, OP28 and CP5 show high EWC values and consequently high energy consumption that should be reduced.

Figure 5.6 Flowsheet for biodiesel production from Jatropha oil.
Table 5.2 Important open-path and the associated indicators.

Path

MVA

Probability

Path

EWC

Probability

Path

TVA

Probability

OP 28

-269.3

High

OP 16

27.8

Medium

OP 28

- 293.7

High

OP 12

-266.6

High

OP 28

24.5

Medium

OP 12

- 271.6

High

OP 2

-71.2

High

OP 6

19.3

Medium

OP 2

- 71.2

High

OP 29

-25.1

High

OP 11

14.0

Medium

OP 29

- 25.6

High

OP 1

-9.8

High

OP 5

6.4

Low

OP 1

- 10.0

High

OP 33

- 1.1

High

OP 12

5.0

Medium

OP 5

- 6.4

Low

OP 34

- 1.1

High

OP 2

1.0

High

OP 33

- 1.9

High

Table 5.3

Important open-path and the associated indicators.

Path

EWC

Probability

Path

AF

Probability

C5

4.11

Check AF

C5

1.04

High

C6

0.00

High

C6

0.00

Medium

C4

0.00

High

C4

0.00

Medium

In Table 5.4 the most important energy indicators are listed. The high values of TDC show that there is a high potential for improvements and consequently that energy integration can be considered.

The sustainability metrics and the safety index were also calculated (their values are listed in Table 5.6 presented after Step 6).

Table 5.4 Important energy indicators.

Demand

TDC ($/h)

Biodiesel

63

C E3

6

Table 5.5

Indicator sensitivity

analysis

(ISA)

algorithm

results for the biodiesel case

study.

Path

Indicator

Scores

OP 28

MVA

21

OP 12

MVA

21

OP 2

MVA

29

OP 16

EWC

14

OP 28

EWC

21

CP5

EWC

18

CP5

AF

18

5.5.1.4 Step 4: Indicator Sensitivity Analysis (ISA) Algorithm

To apply the ISA algorithm the indicators MVA for OP28, OP12 and OP2, EWC for OP 16, OP 28 and CP 5 were selected as possible target indicators. After applying the ISA algorithm, scores were given to the selected indicators (see Table 5.5).

From Table 5.5 it is seen that from the selected indicators, the MVA indicator related to OP 2 for the Jatropha oil is the most sensitive (highest score). MVA values for OP 12 and OP 28 also present high scores. Consequently, these indicators are considered the target indicators for improvements.

5.5.1.5 Step 5: Process Sensitivity Analysis

From a sensitivity analysis of the operational parameters influencing the target indicators (MVA- OP 2; OP 12; OP 28) it was found that the most significant operational variables are the flowrates of the respective open-paths.

5.5.1.6 Step 6: Generation of New Design Alternatives

To generate a new sustainable design alternative, the first thing to do is to verify in which category the selected parameter is included (see Step 6 of the Methodology Section 5.3.1.6). For OP 2, it was found that the operational variable is related to the reduction of an open- path flowrate of a raw material. This pointed to a reduction of the OP 2 flowrate by considering, the recycling of the Jatropha oil. To recycle Jatropha oil, a purge has been considered to avoid the build- up of undesired compounds. Regarding OP 12, it was found that this operational variable is related to the increase of an open- path flowrate of a product, which means that the separation process to recovery biodiesel needs to be improved. The distillation column separation has been optimized in terms of number of trays and reflux ratio. For OP 28 it was found that this compound can be sold out if a required purity is achieved. Consequently, the liquid-liquid extraction separation process needs to be improved. The temperature has been optimized and the required purity has been achieved. Energy integration has been considered. The streams leaving the distillation columns were used to heat the streams entering the distillation column. Energy has been saved with this approach.

Using this information the process was simulated again with all the suggested improvements in order to validate the new design alternative.

For the new sustainable design alternative, which consists of the recycling of Jatropha oil, improved distillation process and improved liquid-liquid extraction process, the following improvements were achieved. The profit increased by 27%, the water metric was improved by 39% and 44%, the energy metric was improved by 71% and 74%, and the material metric was improved by 10% and 17%. The environmental impact has been improved by 11%. The rest of the performance criteria parameters have remained constant (confirming a non-trade-off solution). All the values for the performance criteria are listed in Table 5.6. The improvements in the target indicators are listed in Tables 5.7 and 5.8.

Table 5.6 Comparison of the performance criteria between the 'reference' design and the new sustainable design alternative for the biodiesel production case study.

Sustainability Metrics

Base case

New design

Improvement

Total net primary energy usage rate (GJ y-1)

31281

9 991

68%

% Total net primary energy sourced from renewables

0.9999

0.999 7

0.02%

Total net primary energy usage per kg product (kJ kg-1)

1459

417

71%

Total net primary energy usage per unit value added (kJ $-1)

1.2

0.3

74%

Total raw materials used per kg product (kgkg-1)

0.41

0.37

10%

Total raw materials used per unit value added (kg $-1)

0.00034

0.000 28

17%

Net water consumed per unit mass of product (kgkg-1)

1.7

1.0

39%

Net water consumed per unit value added (kg $-1)

0.001 4

0.000 8

44%

Safety index

22

22

0%

WAR

114

102

11%

Profit ($y-1)

3 005 427

3 827084

27%

Table 5.7 Mass target indicators improvements.

MVA ($/h)

Base-Case

New Design

Improvements

OP 2

-8.7

- 1.8

79%

OP12

-33

- 1

97%

OP28

-33.7

- 31

8%

Table 5.8 Energy target indicators improvements.

TDC ($/h)

Base case

New design

Improvements

Biodiesel

63

11

83%

C E3

6

0

100%

Table 5.9 CO2 emission for the base case and for the new sustainable design alternative for the biodiesel production case study.

CO2 emission (kg/y)

Initial

New

Improvement

Fuel oil

2418021

772 304

68%

Natural gas

2005112

640423

68%

The energy reduction will of course reduce the CO 2 emission. For this case study it is assumed that the furnace uses fuel oil or natural gas to produce steam. The CO2 emission has been determined for the base case and for the new design alternative, taking into consideration the two established scenarios (see Table 5.9)

Table 5.9 shows that an improvement of 68% in the CO 2 emission has been achieved with the new proposed alternative. These results show that a more sustainable design alternative has been found.

Detailed simulation and process data can be obtained from the corresponding author.

Batch Processes: Insulin Case Study

The insulin process [16] is divided into four sections:

1) Fermentation: Here the E.coli cells are used to produce the Trp-LE'-MET-proinsulin precursor of insulin, which is retained in the cellular biomass. Fermentation takes place in order to achieve the desired biomass.

2) Primary Recovery: In this section a high pressure homogenizer is used to break the cells and release the inclusion bodies. Then with a set of centrifuges and solvents the inclusion bodies are recovered with a higher purity.

3) Reactions: In this part of the process there is a sequence of reactions until the production of insulin.

4) Final Purification: Finally, a purification sequence based on multimodal chromatography, which exploits differences in molecular charge, size, and hydrophobicity, is used to isolate biosynthetic human insulin. The crystallization of insulin is the last step of the process.

The flowsheet for the insulin production process is shown in Figure 5.7.

5.5.2.1 Step 1: Collect the Steady-state Data

The required detailed process data for the insulin synthesis plant was taken from a simulation available on SuperPro Designer (2008) software package. The prices and costs were taken from [16], where the insulin production simulation is described in detail.

5.5.2.2 Step 1A: Transform Equipment Flowsheet in an Operational Flowsheet

The equipment flowsheet consists of 31 units, which can be seen in Figure 5.7 (some equipments are represented more than once in the flowsheet; they have however, the same name). Taking into account the sequence of operations, the operational flow diagram is determined. The operational flow diagram has 92 operations, 169 streams and 38 compounds.

5.5.2.3 Step 2: Flowsheet Decomposition

For this case study the operations flow diagram decomposition generated 418 closed-paths, 1022 open-paths and 3344 accumulation-paths.

5.5.2.4 Step 3: Calculate the Indicators, the Sustainability and the Safety Metrics

For the entire set of flow-paths, the full set of indicators was calculated, except for some batch compound indicators whose data were not available. Due to the large size of the flowsheet it is not be possible to present or discuss all the modifications to improve the whole process. Therefore, in the remaining steps, only section 1 and section 3 are highlighted with respect to improvement of their mass indicators and batch indicators.

The most sensitive mass indicators for those sections were selected and they are listed for the selected sections in Table 5.10.

For this case study the most sensitive indicators are the MVA-material value added, for the open-paths listed in Table 5.10. They have very negative values, which means that a lot of money is wasted from the time the materials (compounds) enter the system to the time they exit. The energy consumption and the recycling in the process do not allow very high potential for improvements when compared with the very high values of MVA (see also the EWC values in Table 5.10 ).

The most sensitive batch indicators were selected and they are listed for each section in Table 5.11 .

Operations V-102R, V-103(P8)R, V-105R and V-111R present high values of OTF when compared with the other operations, which means that these operations are spending too much time to execute their respective process operation. V-102R and DS-101(P9) have high values of OEF when compared with the other operations. This indicates that these two operations have high energy consumption. These

Insulin Production Flowsheet

Primary Recovery Section

Insulin Production Flowsheet

Primary Recovery Section

Formic Acid Flowsheet
Figure 5.7 Flowsheet for insulin production.
Table 5.10 Mass indicators and their calculated values for the insulin production case study.

Section

OP

Path

Component

MVA

EWC

TVA

(103$/y)

(103$/y)

(103$/y)

Fermentation

OP

37

S4-S26

Water

-22 560

68.90

- 22 629

(Section 1)

Reactions

OP

620

S79-S80

Urea

-205917

0.00

- 205 917

(Section 3)

OP

591

S54-S60

Formic acid

-137334

0.34

-137334

OP

613

S77-S80

Urea

-90375

0.00

- 90 375

OP

657

S62-S69

HCl

- 74 542

0.01

- 74 542

OP

659

S62 - S80

HCl

- 51 070

0.01

- 51 070

OP

598

S43 - S49

Urea

-43 552

0.01

-43 552

OP

615

S77 - S92

Urea

- 37 885

0.02

- 37 885

OP

316

S85 - S92

WFI

- 21 519

17.82

- 21 537

OP

313

S79 - S80

WFI

- 19 808

0.00

- 19 808

OP

173

S50 - S49

WFI

- 17 663

0.00

- 17 663

OP

403

S103-S104

WFI

- 16 328

0.00

- 16 328

OP

335

S91 - S92

WFI

-13144

0.00

-13144

OP

292

S77 - S80

WFI

- 10 561

0.00

- 10 561

OP

721

S103-S104

NaCl

- 12 210

0.00

- 12 210

Table 5.11 Batch indicators and their calculated values for the insulin production case study.

Section Operation

TFVF OTF

OEF

AP

Compound

TF

EF

Fermentation V-102R

0.83 0.07

0.33

230

Oxygen

27058

0.06

(Section 1)

231

Glucose

9 884

0.07

232

Salts

63021

0.00

233

Water

66039

3.06

234

Biomass

Non-defined

Non - defined

235

Ammonia

66244

0.00

236

CO2

44 668

0.10

Reaction V - 103(P8) R

0.87 0.03

0.01

943

Cont Proteins

20410.07

Not available

(Section 3)

944

IBs

90061.83

Not available

945

Trp-proinsulin

Non - defined

Not available

V - 105 R

0.74 0.04

0.002

1388

NaSO3

23.49

Not available

1389

Na2O6S4

100.90

Not available

1402

Denatured protein

660 080.16

Not available

1403

Proinsulin-SSO3

Non - defined

Not available

indicators show high potential for improvements and their values should be reduced. The options for improvements in the selected section are analyzed below.

• Fermentation (Section 1): In this section the most critical points are related to the waste water, which is produced as a by -product in the main reaction, so there is little likelihood of reducing it. Consequently this is not the best choice for a process improvement. Regarding the batch indicators it can be seen that a very high value of OTF has been calculated for the fermentation operation (V-102R). Analyzing the compound indicators for this operation it is seen that ammonia is the compound which is limiting the operation time. Consequently to improve the fermentation process it would be necessary to take into consideration the ammonia concentration and the related parameters, which influence the rate of the reaction (this point is further discussed in steps 5 and 6).

• Reactions (Section 3): This section involves many solvents (urea, WFI, formic acid, HCl, NaCl) which are not recovered and recycled within the process. The best option to improve these indicators, and consequently the process, is to recover and recycle the solvents. For some of them it might not be economically feasible. Some waste solvents, however, may be sold to other users. For example, urea can be further processed and used as nitrogen fertilizer - 16]- Here, two operations, V-103 R and V-105 R, indicate high values of OTF, which point out that their operation time should be reduced. Regarding the compound indicators for these two operations, it is possible to see from Table 5.11 that IB and denatured proteins are the compounds causing the high time consumption. In order to decrease the time factor, it is necessary to analyze the rate of reaction conditions.

The sustainability metrics and the safety index were also calculated (their values are listed in Table 5.13 presented after Step 6).

5.5.2.5 Step 4: Indicator Sensitivity Analysis (ISA) Algorithm

To apply the ISA algorithm the indicators listed in section 3 of Table 5.10 were selected as possible target indicators. After applying the ISA algorithm it is seen that from the selected indicators, the MVA indicator related to OP591 for formic acid is the most sensitive. Consequently, this indicator is considered the target indicator for improvements (see Table 5.12, row highlighted with bold letters).

For batch indicators, the most sensitive indicator in section 1 is the TF of ammonia in the fermentation operation (V-102R).

5.5.2.6 Step 5: Process Sensitivity Analysis

From a sensitivity analysis of the operational parameters influencing the target indicator (MVA - OP591) it was found that the most significant operational parameter is the flowrate of OP591.

The fermentation process time is mainly dependent on the specific cell growth rate, which is represented by the following equation ([17])

Table 5.12 Indicator sensitivity analysis (ISA) algorithm results for the insulin production case study.

Path Indicator Compounds Scores

Table 5.12 Indicator sensitivity analysis (ISA) algorithm results for the insulin production case study.

Path Indicator Compounds Scores

OP 721

MVA

NaCl

2

OP 620

MVA

Urea

3

OP 613

MVA

Urea

2

OP 403

MVA

WFI

2

OP 335

MVA

WFI

8

OP 313

MVA

WFI

5

OP 292

MVA

WFI

3

OP 173

MVA

WFI

4

OP 316

MVA

WFI

10

OP 591

MVA

Formic acid

15

OP 615

MVA

Urea

10

OP 598

MVA

Urea

11

OP 657

MVA

HCl

7

OP 659

MVA

HCl

6

((kGlu cos e + CoGlu cos e ) {l0l + Coo2 ) (feNHs + ConH3 ) (kHPm + CoH3PO4 )

In Eq. (5.6), jug is the specific cell growth rate, ^gmax is the maximum specific cell growth rate, k is the monod constant for each compound and Co is the concentration.

To analyze the operational parameters that influence the batch target indicator (TF), Eq. ( 5.6) was used and it was possible to verify that the ammonia (NH3) concentration is the most significant parameter in order to reduce the time of the reaction.

5.5.2.7 Step 6: Generation of New Design Alternatives

To generate a new sustainable design alternative, the first thing to do is to verify in which category the selected parameter is included (see Section 5.3.1.6). It was found that the operational parameter is related to the reduction of an open-path flowrate. This pointed to a reduction of the OP591 flowrate by considering, the recycle of the formic acid. To recycle formic acid, a separation operation needs to be inserted in order to purify/recover this compound. Applying the process separation algorithm of Jaksland and Gani ( 12] ( a set of feasible separation techniques for the recovery of formic acid coming from stream S60 was identified. Pervapora-tion was selected as the separation operation, because it involves lower operational costs when compared with the other separation techniques and it does not need external compounds for the separation. In the literature, Nakatani et al. [18], found that membranes such as aromatic imide polymer asymmetric, are available to purify/recover formic acid from water (which is the mainly impurity compound in S60). To estimate the selectivity of the membrane, it is assumed that this system

(membrane to separate) has the same behavior as that of a similar mixture considered by Huang et al. - 19]. Using this information the process was simulated again in order to validate the new design alternative. To reduce the fermentation time the concentration of ammonia needs to be increased. The concentration was increased by 2%, and 0.2% of fermentation time reduction was achieved, which is not a significant improvement. This indicates that the fermentation process is already optimized and nothing could be done to improve it. Also, the fermentation operation has more constraints that cannot be violated without changing the enzyme.

For the new sustainable design alternative, which consists of the recycling of formic acid, the following improvements were achieved. The profit increased by1.98%, the water and the energy metrics per value added improved by 2%. The material metrics improved by 2% and 4% respectively per kg of final product and per value added. Finally, the environmental impact output was improved by 31.7%. The rest of the performance criteria parameters have remained constant. All the values for the performance criteria are listed in Table 5.13. The target indicator was improved by 99.9% (MVA-OP591 Initial = -1.37 x 108$/y, Final = -169 x 103$/y). Note that MVA should be positive (or less negative). Clearly, the new design has made this target indicator less negative.

In this case there is no energy reduction. For this case study the supplied material has been reduced. However the CO2 emission measured per value added has been reduced. This means that for a greater profit it was not necessary to increase the CO2 emission. The CO2 emission has been determined for the base case and for the new design alternative, taking into consideration the two established scenarios (see Table 5.14).

Table 5.13 Comparison of the performance criteria between the 'reference' design and the new sustainable design alternative for the insulin production case study.

Metrics

Initial

Final

Improvement

Total net primary energy usage rate (GJ/y)

26 727

26 727

0%

% Total net primary energy sourced from renewables

0.72

0.72

0%

Total net primary energy usage per kg product (kJ kg-1)

292 397

292 397

0%

Total net primary energy usage per unit value added (kJ/$)

4.55 x 10-4

4.46 x 10-4

1.94%

Total raw materials used per kg product (kg/kg)

43 029

42083

2.20%

Total raw materials used per unit value added (kg/$)

6.70 x 10-5

6.42 x 10-5

4.10%

Fraction of

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