ABSTRACT
This
study presents ASPEN Base Case Simulation (ABCS), preliminary process design
with filtration integration, techno-economics and uncertainty analysis of
bioclarified water production from petroleum wastewater. ABCS, scale-up design
and economics were performed using inherent design and costing algorithms in
ASPEN Batch Process Developer (ABPD) V10. The process profitability indices
such as Net Present Value (NPV), Internal Rate of Return (IRR), Return on
Investment (ROI) and Payback Time (PBT) were evaluated in a user-defined
developed Microsoft-excel version 2018. Predictive models for predicting and
optimizing techno-economic parameters: return on investment (ROI), payback time
(PBT) and production rate (PR) were achieved in RSM via Box-Behnken Design (BBD) technique
of Design Expert V13. The regression models gave R2 values of
0.9984, 0.9920 and 0.8867 for
return on investment, payback time and production rate respectively. Monte
Carlo Simulation in Crystal Ball Oracle software was used to perform the profitability
sensitivity and uncertainty analyses. The annual production target (600,000litres/year)
scale-up simulation results gave batch size 406litre/batch, annual number of
batches produced 1469batches/year. Base
case capacity results showed that the total capital investment, NPV, IRR, ROI
and PBT are $631485, $68932.18, 9%, 15.8% and 6.33yrs respectively. Sensitivity analysis shows that selling price
has the highest contribution for both the NPV and the IRR respectively. The
certainty of the base case model after 30000trials was 99.98% for NPV, 90.89%
for IRR and 61.23% for production rate. This study showed that petroleum
wastewater BFS scale-up design is feasible.
TABLE
OF CONTENTS
Cover page
Title page i
Declaration ii
Certification iii
Dedication iv
Acknowledgements v
Table of contents vi
List of tables viii
List of figures ix
Abbreviations/Nomenclature xi
Abstract xiii
CHAPTER 1
INTRODUCTION
1.1 Background of study 1
1.2 Statement of problem 6
1.3 Aim of study 7
1.4 Objectives 7
1.5 Significance of the study 8
1.6 Scope of the study 8
CHAPTER 2
LITERATURE
REVIEW
2.1 Petroleum wastewater 9
2.2 Petroleum wastewater characteristics 10
2.3 Petroleum wastewater treatment
technologies 11
2.3.1 Physical treatment 12
2.3.2 Membrane 12
2.3.3 Coagulation/flocculation 15
2.3.4. Electro-coagulation 17
2.3.5. Adsorption 18
2.3.6. Physical-chemical
treatment 19
2.3.7 Chemical treatment 19
2.3.8 Biological treatment 20
2.3.8.1 Aerobic biological processes 21
2.3.8.2 Anaerobic biological process 21
2.3.9 Aerated lagoons 22
2.3.10 Activated sludge process 22
2.3.11 Biofilm-based reactor 22
2.4 Filtration process integration 23
2.5 Techno-Economic Analysis 24
2.5.1 Techno-economic analysis of bioclarified
water production 24
2.5.2 Process modelling and simulation 25
2.5.3 Response
Surface Methodology (RSM) 26
2.5.4 Process economics, sensitivity and
uncertainty analyses 27
2.6 Review of related works 29
2.7 Research gap 30
CHAPTER 3
MATERIALS AND
METHOD
3.1 Aspen
batch base case process simulation environment
31
3.2 Simulation procedures using aspen batch
process developer 32
3.2.1 Mixing 33
3.2.2 Coagulation stage 33
3.2.3 Flocculation stage 33
3.2.4 Settling stage 34
3.2.5 Filtration stage 34
3.3 Base
case process description and scale-up process design 34
3.4 Process economics and profitability
evaluation 36
3.5 Techno‑economic modelling and optimization study 38
3.5.1 Optimization
study methodology 42
3.6 Monte Carlo simulation uncertainty and
sensitivity analyses 42
CHAPTER 4
RESULTS AND
DISCUSSION
4.1
Process base case scale-up
simulation and annual production design results 44
4.2
Process economics results 48
4.2.1 One-factor at a time (OFAT) profitability
sensitivity analysis 50
4.2.2 Effect
of discounted rate cumulative cash flow
diagram 53
4.3 RSM techno‑economic model
fitting 54
4.3.1: Effect of the cost factors on PBT 58
4.3.2: Effect of the cost factors on ROI 62
4.3.3: Effect of the cost factors on production rate 66
4.4: Bioclarified
water production optimization studies 70
4.5: Profitability uncertainty and sensitivity
results 73
CHAPTER 5
CONCLUSION AND
RECOMMENDATIONS
5.1 Conclusion 79
5.2 Research recommendations 80
5.3 Contributions to knowledge 80
REFERENCES 81
APPENDIX 95
LIST
OF TABLES
Table
Title Page
3.1: Properties of independent variable selected for BBD method 30
3.2: The BBD experimental design 30
4.1: Stream balance of
bio-clarified water production from petroleum wastewater46
4.2: Batch process design throughput parameters
of bio-clarified water production47
4.3: Process base-case economic parameters of
bioclarified water production
from PPW
50
4.4: The BBD experimental design matrix 56
4.5: Fit summary for the
production of bioclarified water from petroleum
wastewater 57
4.6: ANOVA Results for PBT 60
4.7: ANOVA Results for ROI 64
4.8: ANOVA Results for Production rate 68
4.9: Optimization criteria for bioclarified
water production 71
LIST
OF FIGURES
Figure Title Page
3.1: Process flowsheet for bio-clarified water reclamation from PPW 26
4.1: Distribution of
ASPEN-installed cost factors for total capital investment 49
4.2a: variation of the project
total capital investment with profitability indices 52
4.2b: variation of the project annual
production cost with profitability indices 52
4.2c: Effect of discount rate on
PBT, NPV, ROI and IRR 53
4.3: Profitability
evaluation of bio-clarified water production using cumulative cash
flow diagram 54
4.4: Design expert plot, predicted vs. actual
plot for (a) PBT (b) ROI
(c) Production rate 57
4.5: Design
expert plot; response surface 3D plot for PBT with: (a) AB (b) AC
(c) AD (d) AE (e) BC (f) BD (g) BE (h) CD (i) CE (j) DE 62
4.6:
Design expert plot; response surface
3D plot for ROI with: (a) AB (b) AC
(c) AD (d) AE (e) BC (f) BD (g) BE (h) CD
(i) CE (j) DE 66
4.7:
Design expert plot; response surface
3D plot for Production rate with: (a) AB
(b) AC (c) AD (d) AE (e) BC (f) BD (g) BE
(h) CD (i) CE (j) DE 70
4.8: Optimization
results ramp for bioclarified water production. 72
4.9a: Contribution of input
variable variation on NPV 74
4.9b: Contribution of input
variable variation on IRR 74
4.9c: Contribution of input
variable variation on production rate 75
4.10a: Uncertainty level (NPV)
for bioclarified water production from PPW 77
4.10b: Uncertainty level (IRR)
for bioclarified water production from PPW 78
4.10c: Uncertainty level
(production rate) for bioclarified water production from PPW 78
NOMENCLATURE
OF ABBREVIATIONS
ABPD - Aspen Batch Process Developer
ASDM - activated sludge digestion model
BAF - biological aerated filter
BBD - Box-Behnken Design
BFS - Biocoagulation-Flocculation-Sedimentation
BOD - Biochemical oxygen demand
BTEX
- chemicals
(benzene, toluene, ethylbenzene and xylene)
CAPD - Computer-Aided Process Design
CCFD - Cumulative Cash Flow Diagram
CF - Coagulation-Flocculation
CF–MBR
- cross-flow membrane bioreactor
CFS - Coagulation-Flocculation-Sedimentation
COD - Carbon Oxygen Demand
COD - Chemical Oxygen Demand
DPC - Direct Production Cost
FCI - Fixed Capital Investment
HF-MBR - hollow-fiber membrane bioreactor
HRT - hydraulic retention times
IPC - Indirect Production Cost
IRR - Internal Rate of Return
IRR - internal rates of return
MAD - Mean Absolute Deviation
MAPE - Mean Absolute Percentage Error
MBR - membrane bioreactor
MF - microfiltration
MSE - Mean Square Error
NA - naphthenic acids
NF - Nanofiltration
NPV - Net Present Value
NPV - net present values
NTU
- Nephelometric Turbidity unit
OCB
- Oracle Crystal Ball
PAH - Poly-Aromatic, Phenol and
Hydrocarbons
PBT - Payback Time
PBT - payback time
PC - Production
Cost,
PPW - Petroleum Produced Water
PW - Produced Water
RMSE - Root Mean Square Error
RO - Reverse Osmosis
ROI - return on investment
RSM - Response Surface Methodology
SS - Suspended Solids
TCI - Total Capital Investment
TCI - Total Capital Investment
TDP - Total Dissolved particles
TEA - Techno-economic analysis
TMP - Trans-membrane pressure
TPDC - Total Plant Direct Cost
TPIC - Total Plant Indirect Cost
TSS - Total Suspended Solids
UF - Ultrafiltration
WC - Working Capital
LIST OF APPENDIX
APPENDIX I: Optimization solutions for bioclarified water
production
APPENDIX II: Definition of terms
KUFRE, O (2023). Computer–Aided Scaleup Process Integration, Economic Feasibility And Uncertainty Evaluation Of Bioclarified Water Recovery From Petroleum Waste Water. Mouau.afribary.org: Retrieved Dec 25, 2024, from https://repository.mouau.edu.ng/work/view/computeraided-scaleup-process-integration-economic-feasibility-and-uncertainty-evaluation-of-bioclarified-water-recovery-from-petroleum-waste-water-7-2
OSOH, KUFRE. "Computer–Aided Scaleup Process Integration, Economic Feasibility And Uncertainty Evaluation Of Bioclarified Water Recovery From Petroleum Waste Water" Mouau.afribary.org. Mouau.afribary.org, 07 Sep. 2023, https://repository.mouau.edu.ng/work/view/computeraided-scaleup-process-integration-economic-feasibility-and-uncertainty-evaluation-of-bioclarified-water-recovery-from-petroleum-waste-water-7-2. Accessed 25 Dec. 2024.
OSOH, KUFRE. "Computer–Aided Scaleup Process Integration, Economic Feasibility And Uncertainty Evaluation Of Bioclarified Water Recovery From Petroleum Waste Water". Mouau.afribary.org, Mouau.afribary.org, 07 Sep. 2023. Web. 25 Dec. 2024. < https://repository.mouau.edu.ng/work/view/computeraided-scaleup-process-integration-economic-feasibility-and-uncertainty-evaluation-of-bioclarified-water-recovery-from-petroleum-waste-water-7-2 >.
OSOH, KUFRE. "Computer–Aided Scaleup Process Integration, Economic Feasibility And Uncertainty Evaluation Of Bioclarified Water Recovery From Petroleum Waste Water" Mouau.afribary.org (2023). Accessed 25 Dec. 2024. https://repository.mouau.edu.ng/work/view/computeraided-scaleup-process-integration-economic-feasibility-and-uncertainty-evaluation-of-bioclarified-water-recovery-from-petroleum-waste-water-7-2