# Development of Chemical Process Design and Control for Sustainability

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Process Sustainability Assessment and Design

## 3. Novel Advanced Control Approach

_{0}) or the leader’s trajectory for the initialization of the algorithm. In addition, two important parameters that define the leader-follower local interactions need to be specified, the pursuit time, Δ, and the sampling time, δ.

## 4. New Approach for Process Modeling and Advanced Control for Sustainability

#### 4.1. Bioethanol Manufacturing Process Model

#### 4.2. Case Study: Fermentation for Bioethanol Production System

#### 4.2.1. Open-Loop Dynamics of Fermentation Process

^{−1}, and ${D}_{j}$ was set to zero. For these simulations, the system was integrated using the $\mathrm{ode}15\mathrm{s}$ solver in MATLAB (Version 8.3, MathWorks, Inc., Natick, MA, USA) for the given differential and algebraic equations that were solved simultaneously. Figure 5 shows the concentration profiles of the key component, biomass, substrate, product in the fermentor and membrane sides, as well as temperature profiles in the fermentor for the open-loop simulations with different membrane dilution rates. As expected, a higher ${D}_{m,in}$ can efficiently reduce or even eliminate the oscillatory behavior of the concentrations and can enhance the substrate conversion rate. This can be explained by the reduction of the end-product inhibition when more ethanol is removed through the ethanol-selective membrane at the cost of using more fresh water. It is important to note that this fermentation process has multiple equilibrium states as a consequence of autocatalytic reactions [40]. To obtain an optimal steady-state operating condition in terms of sustainability and examine the effectiveness of the proposed biomimetic controller, an open-loop case is chosen as the benchmark, and then, higher and lower set points are used for closed-loop simulations. Through the comparison of the GREENSCOPE indicators for the benchmark and closed-loop simulations, a systematic decision can be made in terms of moving the process operation in the right direction towards a more sustainable steady state.

#### 4.2.2. Closed-Loop Results and Discussion

^{−1}. The new achieved steady states are evaluated and compared in terms of sustainability using selected GREENSCOPE indicators. Based on the results of the first two cases, Cases 3 and 4 are then performed to locate the optimal steady-state operation for a higher ${D}_{in}$ of 0.2 h

^{−1}, which corresponds to a higher volumetric productivity for the fermentor. For all simulations, the parameter values in Table 1 are kept constant.

^{−1}is chosen as the benchmark since it represents the highest achievable product concentration with reduced oscillations, as it approaches the steady state. In particular, for this case, the dynamic behavior in Figure 5 shows oscillations of mid-range amplitudes within 80 h before the system finally achieves its steady state at around 100 h. It is important to note that there is still some substrate left in the reactor at steady state as depicted in the substrate profile of Figure 5. This can be explained by the fact that the environmental conditions in this case, such as temperature and ethanol concentration, are not favorable for a high substrate conversion rate. Thus, there should be some room for improvement of process performance by the implementation of an effective control strategy in terms of efficiency and productivity, if the system is optimized to convert all substrate into product. To attain this goal of increasing the process efficiency, a higher set point for the controlled variable, ${C}_{P}$, of 65 kg/m

^{3}, when compared to the steady-state product concentration of the benchmark case, 57.16 kg/m

^{3}, is used. In addition, an optimal temperature value, 30 °C, for ${T}_{r}$ is employed in the closed-loop simulation. Both open-loop and closed-loop simulations start at the same initial points, and the inlet dilution rate, ${D}_{in}$, is kept at 0.1 h

^{−1}. Figure 6 depicts the closed-loop simulation results for the concentrations of key component, biomass, substrate, product and temperature, as well as the input profiles. Note that, with the implementation of the proposed biomimetic control strategy, the original oscillations are eliminated, and merely a trace of substrate unreacted, 0.043 kg/m

^{3}, is left in the reactor. However, in terms of sustainable performance, the radar plot of Figure 7 shows that the controller implementation only slightly improves three GREENSCOPE indicators in three categories (efficiency, economic, and environmental), reaction yield (RY), water intensity (WI) and economic potential (EP), towards a more sustainable process operation. In addition to Table A1 in the Appendix, more details regarding indicator definition (qualitative and quantitative), data inputs and best and worst case reference values can be found elsewhere [25,26,27]. Another key aspect is the steady-state biomass concentration is 2.50 kg/m

^{3}in the closed-loop simulation, which is higher than the open-loop simulation, 2.31 kg/m

^{3}. This higher value means that more substrate is consumed for biomass growth, rather than for producing ethanol in the new scenario. This fact explains why some of the other indicators, such as resource energy efficient (η

_{E}) and specific resources material costs (C

_{SRM}), do not show improvement even though the substrate conversion rate increased by 5.1%.

^{3}as the set point for the closed-loop scenario and keep the set point of ${T}_{r}$ at 30 °C in Case 2. Figure 8 shows the concentration and temperature profiles, as well as the input profiles for the closed-loop simulation. Compared to the results in Figure 6a, Figure 8a shows that the system reaches the steady state in this case in a shorter time and with a lower substrate concentration of 0.03 kg/m

^{3}. In addition, the steady state ${D}_{m,in}$ increases to 0.61 h

^{−1}, which means that more ethanol is removed by the membrane to keep a lower ethanol concentration in the reactor. All GREENSCOPE indicators except water intensity (WI) in Figure 9, such as reaction yield (RY), environmental quotient (EQ), environmental potential (EP), specific raw material costs (C

_{SRM}), specific energy intensity (R

_{SEI}) and resource energy efficiency (η

_{E}), demonstrate the higher degree of sustainability for the closed-loop scenario. This improvement of sustainability performance can be attributed to the elimination of oscillations and removal of the inhibition effect by the product after the implementation of the biomimetic control strategy.

^{−1}is studied, where the set points are kept at the same values as in Case 2. Figure 10 presents the concentrations of key component, biomass, substrate, product and temperature, as well as the input profiles for the closed-loop simulation in this case. When compared to the results of Case 2, which are depicted in Figure 8, the closed-loop scenario in this case shows that the manipulation of ${D}_{m,in}$ effectively enables the system to achieve a high conversion rate even at high ${D}_{in}$. The residual substrate concentration in the fermentor is now 0.075 kg/m

^{3}, which is slightly higher than that in Case 2 (0.03 kg/m

^{3}). The GREENSCOPE indicators in Figure 11 demonstrate that the specific energy intensity indicator (R

_{SEI}) becomes more sustainable, and the environment and economic indicators for Case 2 and Case 3 overlap each other. Moreover, efficiency indicators for Case 3 are slightly less sustainable than that of Case 2 due to the relatively lower substrate conversion rate.

^{3}in the closed-loop simulation with ${D}_{in}$ of 0.2 h

^{−1}, and then, the process performance is compared to that of Case 3. Figure 12 shows the concentrations of the key component, biomass, substrate, product and temperature, as well as the input profiles for this closed-loop simulation. The radar plot of Figure 13 shows that most selected GREENSCOPE indicators do not change except water intensity (WI), which reduces its score. This can be explained by the fact that there is little room for improvement in terms of sustainability when compared to Case 3, which has a fermentation process with a high level of efficiency. Therefore, the system has reached its limitation in terms of the optimal ${C}_{P}$ set point without compromising the process sustainability.

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## Nomenclature

Variables | Definition/Units |

A_{1}/A_{2} | Exponential factors in the Arrhenius equation |

A_{M} | Area of membrane (m^{2}) |

AI | Analysis indicator |

A_{T} | Heat transfer area (m^{2}) |

C_{i} | Concentration of component i (kg/m^{3}) |

c_{p,r} | Heat capacity of the reactants (kJ/kg/K) |

c_{p,w} | Heat capacity of cooling water (kJ/kg/K) |

D_{in} | Inlet fermentor dilution rate (h^{−1}) |

D_{j} | Cooling water flow rate (h^{−1}) |

D_{out} | Outlet fermentor dilution rate (h^{−1}) |

D_{m,in} | Inlet membrane dilution rate (h^{−1}) |

D_{m,out} | Outlet membrane dilution rate (h^{−1}) |

E_{a1}/E_{a2} | Active energy (kJ/mol) |

K_{S} | Monod constant (kg/m^{3}) |

K_{T} | Heat transfer coefficient (kJ/h/m^{2}/K) |

k_{1} | Empirical constant (h^{−1}) |

k_{2} | Empirical constant (m^{3}/kg·h) |

k_{3} | Empirical constant (m^{6}/kg^{2}·h) |

m_{s} | Maintenance factor based on substrate (kg/kg·h) |

m_{p} | Maintenance factor based on product (kg/kg·h) |

M | Mixer |

MW | Molecular weight (g/mole) |

P_{M} | Membrane permeability (m/h) |

P | Correction factor |

r_{i} | Production rate of component i (kg/m^{3}) |

R | Gas constant |

TI | Temperature indicator |

T_{j} | Temperature of cooling water (K) |

T_{w,in} | Inlet temperature of cooling water (K) |

T_{r} | Temperature of the reactants (K) |

V_{F} | Fermentor volume (m^{3}) |

V_{M} | Membrane volume (m^{3}) |

V_{j} | Cooling jacket volume (m^{3}) |

Y_{sx} | Yield factor based on substrate (kg/kg) |

Y_{px} | Yield factor based on product (kg/kg) |

Greek Symbols | |

${\rho}_{r}$ | Reactants density (kg/m^{3}) |

${\rho}_{w}$ | Cooling water density (kg/m^{3}) |

$\mathsf{\mu}$ | Specific growth rate (h^{−1}) |

${\mathsf{\mu}}_{max}$ | Maximum specific growth rate (h^{−1}) |

$\Delta H$ | Reaction heat of fermentation (kJ/kg) |

Subscripts | |

e | Key component inside the fermentor |

e,_{0} | Inlet key component to the fermentor |

P | Product (ethanol) inside the fermentor |

P,0 | Inlet product to the fermentor |

PM | Product (ethanol) inside the membrane |

PM,0 | Inlet product to membrane |

S | Substrate inside the fermentor |

S,0 | Inlet substrate to the fermentor |

X | Biomass inside the fermentor |

X,0 | Inlet biomass to the fermentor |

## Appendix A

Category | Indicator | Formula | Unit | Sustainability Value | |
---|---|---|---|---|---|

Best Case (100%) | Worst Case (0%) | ||||

Efficiency | Reaction Yield (RY) | $RY=\frac{Massofproduct}{Theoreticalmassofproduct}$ | kg/kg | 1.0 | 0 |

Water Intensity (WI) | $WI=\frac{Volumeoffreshwaterconsumed}{Salesrevenueorvalueadded}$ | m^{3}/$ | 0 | 0.1 | |

Environmental | Environmental Quotient (EQ) | $EQ=\frac{Totalmassofwaste}{Massofproduct}\times Unfriendlinessquotient$ | m^{3}/kg | 0 | 2.5 |

Global Warming Potential (GWP) | $GWP=\frac{TotalmassofC{O}_{2}equivalents}{Massofproduct}$ | kg/kg | 0 | Any waste released has a potency factor at least equal to 1 | |

Economic | Economic Potential (EP) | $EP=Revenue-Rawmaterialcosts-Utilitycosts$ | $/(kg product) | 1.5 | 0 |

Specific Raw Material Cost (C_{SRM}) | ${C}_{SRM}=\frac{Rawmaterialcosts}{Massofproduct}$ | $/kg | 0 | 0.5 | |

Energy | Specific Energy Intensity (R_{SEI}) | ${R}_{SEI}=\frac{Netenergyusedasprimaryfuelequivalent}{Massofproduct}$ | kJ/kg | 0 | 100 |

Resource Energy Efficiency (η_{E}) | ${\eta}_{E}=\frac{Energycontentoftheproduct}{Totalmaterial-inputenergy}$ | kJ/kJ | 0 | 1 |

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**Figure 3.**General structure of the algorithm for the advanced control approach [27]. CL: control law; OCP: optimal control problem.

**Figure 5.**Open-loop simulations: concentration profiles of key component (

**a**), biomass (

**b**), substrate (

**c**) and product in the fermentor (

**d**), the membrane side (

**e**) and the temperature profile (

**f**) for different ${D}_{m,in}$ values.

**Figure 6.**Closed-loop simulation profiles (Case 1): concentrations (

**a**), D

_{m,in}(

**b**), temperatures of the fermentor and jacket (

**c**) and D

_{j}(

**d**).

**Figure 7.**Radar plot with GREENSCOPE (Gauging Reaction Effectiveness for the ENvironmental Sustainability of Chemistries with a multi-Objective Process Evaluator) indicators for the closed-loop and open-loop simulations (Case 1).

**Figure 8.**Closed-loop simulation profiles (Case 2): concentrations (

**a**), D

_{m,in}(

**b**), temperatures of the fermentor and jacket (

**c**) and D

_{j}(

**d**).

**Figure 9.**Radar plot with GREENSCOPE indicators for the closed-loop and open-loop simulations (Case 2).

**Figure 10.**Closed-loop simulation profiles (Case 3): concentrations (

**a**), D

_{m,in}(

**b**), temperatures of the fermentor and jacket (

**c**) and D

_{j}(

**d**).

**Figure 11.**Radar plot with GREENSCOPE indicators for closed-loop simulations with different ${D}_{in}$ (Case 3).

**Figure 12.**Closed-loop simulation profiles (Case 4): concentrations (

**a**), D

_{m,in}(

**b**), temperatures of the fermentor and jacket (

**c**) and D

_{j}(

**d**).

**Figure 13.**Radar plot with GREENSCOPE indicators for the closed-loop and open-loop simulations (Case 4).

A_{1} = 0.6225 | K_{S} = 0.5 kg/m^{3} |

A_{2} = 0.000646 | K_{T} = 360 kJ/(m^{2}∙K∙h) |

A_{T} = 0.06 m^{2} | m_{s} = 2.16 kg/(kg∙h) |

A_{M} = 0.24 m^{2} | m_{P} = 1.1 kg/(kg∙h) |

C_{e,0} = 0 kg/m^{3} | P=4.54 |

C_{x,0} = 0 kg/m^{3} | P_{M} = 0.1283 m/h |

C_{S,0} = 150.3 kg/m^{3} | V_{F} = 0.003 m^{3} |

C_{P,0} = 0 kg/m^{3} | V_{M} =0.0003 m^{3} |

C_{PM,0} = 0 kg/m^{3} | V_{j} = 0.00006 m^{3} |

c_{p,r} = 4.18 kJ/(kg∙K) | Y_{sx} = 0.0244498 kg/kg |

c_{p,w} = 4.18 kJ/(kg∙K) | Y_{Px} = 0.0526315 kg/kg |

E_{a1} = 55 kJ/mol | T_{in} = 30 °C |

E_{a2} = 220 kJ/mol | T_{w,in} = 25 °C |

k_{1} = 16.0 h^{−1} | ∆H = 220 kJ/mol |

k_{2} = 0.497 m^{3}/(kg∙h) | ρ_{r} = 1080 kg/m^{3} |

k_{3} = 0.00383 m^{6}/(kg^{2}∙h) | ρ_{w} = 1000 kg/m^{3} |

© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).

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**MDPI and ACS Style**

Li, S.; Mirlekar, G.; Ruiz-Mercado, G.J.; Lima, F.V.
Development of Chemical Process Design and Control for Sustainability. *Processes* **2016**, *4*, 23.
https://doi.org/10.3390/pr4030023

**AMA Style**

Li S, Mirlekar G, Ruiz-Mercado GJ, Lima FV.
Development of Chemical Process Design and Control for Sustainability. *Processes*. 2016; 4(3):23.
https://doi.org/10.3390/pr4030023

**Chicago/Turabian Style**

Li, Shuyun, Gaurav Mirlekar, Gerardo J. Ruiz-Mercado, and Fernando V. Lima.
2016. "Development of Chemical Process Design and Control for Sustainability" *Processes* 4, no. 3: 23.
https://doi.org/10.3390/pr4030023