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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 0.99840.9920 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.


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




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




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 Aerobic biological processes                                                                           21 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




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



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




5.1       Conclusion                                                                                                      79

5.2       Research recommendations                                                                            80

5.3       Contributions to knowledge                                                                           80                                                                                           

REFERENCES                                                                                                        81

APPENDIX                                                                                                               95






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









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



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













APPENDIX I:           Optimization solutions for bioclarified water production

APPENDIX II:          Definition of terms











1.1       Background of Study

Water, as generally described, is essentially important for human existence and it is the most necessary resource for the survival of all living species as well as for use in various industrial purposes (Adeleye et al., 2016). Increase in demand for water supply coupled with population growth and the quest for it in diverse industrial productions has ultimately widened the gap between its demand and supply. Making clean and safe water available for various human activities remains the major challenge in the global community as its shortages could offer deleterious consequences (Maher et al., 2014; Guppy and Anderson, 2017). Anthropogenic activities have consequently contaminated a number of fresh water sources (Tawakkoly et al., 2019).

Petroleum wastewater, otherwise called Produced Water (PW), has been adjudged to be the highest by-product generated during oil and gas operations and contains complex mixtures of both inorganic and organic compounds. PW makes largest volumes in waste streams during oil and gas production operations (Stephenson, 1992; Krause, 1995). This wastewater is therefore a mixture of formation water, injection water, aqueous residues of treatment chemicals, chemical additives from the drilling and some of them contain toxic properties (Danforth et al., 2020). Also contained in the PW are: demulsifiers, biocides, corrosion inhibitors from oil fields (Jiménez et al., 2018) methanol and diethylene glycol, others are: dissolved and dispersed oils. These are mixtures of hydrocarbons (benzene, ethylbenzene, xylenes, toluene, poly-aromatic, phenol and hydrocarbons (PAH)) from gas fields (Igunnu et al., 2014). 

PW has complex compositions and is characteristically highly turbid but its compositions are classified into inorganic and organic compounds (Raza et al., 2019; Klemz et al., 2020). PW affects the environment negatively, this increases public health concerns of ecosystem when it mixes with various water bodies (Raza et al., 2019). The dissolved and dispersed oil contents in produced water are greatly dangerous to the environment and their concentrations could be very high at some oil fields in Nigeria (Menkiti and Ezemagu, 2015). Treatment of petroleum wastewater is necessary in order to recycle the effluent for possible wastewater reuse and to also comply with the stringent legislative environmental regulations governing discharge of wastewater particularly in most developing countries.

Treatments methods of PW are grouped into three types: physical method, chemical method and biological method. The nature of PW is complex in characteristics and its treatments techniques require application of the integrated systems in order to remove necessary contaminants. These methods include Chemical oxidation (Hu et al., 2015), Biological techniques (Wang et al., 2015), Coagulation (Abu-hassan, 2009; Farajnezhad and Gharbani, 2012; El-Naas et al., 2009) and Adsorption (Al Hashemi, 2015). In addition, new technologies have also been reported such as Microwave-assisted catalytic wet air oxidation (Sun et al., 2008) and Membranes (Shariati et al., 2011; Yuliwati et al., 2011). Thus, the conventional treatment methods need multistage process treatments. The first stage consisting of pre-treatment, which includes mechanical and physicochemical treatments followed by the second stage which is the advanced treatment of the pre-treated wastewater.

Physical methods include: sedimentation, filtration and reverse osmosis while chemical processes include: coagulation, flocculation, pH adjustments (Alkhudhiri et al., 2019; El-Ghonemy, 2012). Coagulation-Flocculation (CF) together with Sedimentation (CFS) treatment techniques have been considered as preferred and common primary treatment method for treating turbid industrial wastewater (Hu et al., 2015). CFS is also an integrated physico-chemical solid-liquid separation process used for separating dissolved, colloidal and suspended particles from waterbody. Coagulation involves addition of coagulants (chemicals) to turbid water in order to destabilize stable dispersed particle within waterbody while flocculation aggregates destabilized particles for possible settling (sedimentation). Convectional coagulants (inorganic chemicals) have been utilized for CFS owing to its proven efficiency and performance, however, their applications have been associated with environmental and public health-related concerns (Okolo et al., 2016). Application of synthetic chemicals for CFS also produces large volume of sludge with little or no biodegradability tendency.  On contrary, previous study reported that utilization of biocoagulants for industrial wastewater is efficient, environmentally compatible and sustainable (Kurniawan et al., 2020).

Research findings have shown better improvement of treatment in the removal of turbidity when filtration unit operations (microfiltration and ultrafiltration) are integrated into CFS system (Bouchareb et al., 2020; Thorat and Sonwani, 2022). Filtration Operation separates non-settleable solids from CFS-treated wastewater by passing it through porous media. Formentini-Schmitt et al. (2013) studied dairy industrial wastewater bioclarification using combined coagulation/flocculation/sedimentation with ultrafiltration method. In the said study, it was documented that integrated CFS-ultrafiltration process removed 99% turbidity.

Yimratanabovorn et al. (2018) also reported that combined coagulation–flocculation plus ultrafiltration process had the highest performance of COD, turbidity and colour removal efficiencies than the single CF and the stand-alone ultra-filtration process. Nazia et al. (2021) further reported integration of ultrafiltration membrane operation with coagulation process for old industrial landfill leachate treatment efficiency. The said study confirmed that 70% clarified water was reclaimed for domestic and industrial uses. Furthermore, Optimization of hybrid coagulation-filtration operating conditions for optimal oil removal from oil-water emulsion wastewater was performed by Almojjly et al. (2019) and the result presented optimal conditions for integration of filtration and also confirmed that integration of filtration into CFS increases the removal efficiency of the dissolved particles.  

Available literatures revealed that petroleum wastewater treatments through biocoagulation-Flocculation-Sedimentation (BFS) techniques have been confined to laboratory practices despite its efficiency and cost-effectiveness for wastewater treatments. Menkiti and Ezemagu, (2015) and Menkiti et al. (2016) demonstrated application as well as performance of Tympanotonos Fuscatus and mucuna seed-based bio-coagulants for BFS of Petroleum Produced Water (PPW). The investigations established process optimal conditions and kinetic parameters for possible scale-up process design and economics. Soft-computing prediction and optimization of petroleum wastewater BFS were also reported by Ezemagu et al. (2021). Ejimofor et al. (2022) applied novel Egeria radiate shell biocoagualnt for deturbidization of PPW using coagulation-flocculation technique. It was reported that the efficiency of total dissolved particles removal is highly practicable. The foregoing studies on petroleum produced effluent BFS has opened up valuable laboratory experimental information (biocoagulants properties and process operating conditions) needed for understanding of BFS system dynamics. More so, available data obtained from the documented bibliographic reports are useful for BFS-process system engineering studies and that is the foundation of this research.  However, conceptual proof-of-concept process scale-up simulation, process design and integration of Biocoagulation Flocculation Sedimentation of PPW in possible process development for the future commercialization has not been documented throughout the pool of scientific literature.

Conceptual process design and integration have been greatly achieved with the help of Computer-Aided Process Design (CAPD) techniques. This techniques entail process systems modelling, simulation and optimization. Process simulation encompasses the use of a computer software to carry out steady-state mass balancing, sizing of process equipment, process scale-up and economic evaluation of the process. Computer applications have been used by process scientists and engineers in the area of process simulation and design due to its ability to solve complex mathematics problems as well as analyzing industrial unit operations and production processes (Dursun et al. 2018).  

Previous researches have also shown that different commercial computer-aided simulators: ASPEN HYSYS, ASPEN Batch Process Developer (ABPD), ASPEN Plus, Super Pro Designer etc. could be used to perform process simulation, mass and energy balances, equipment sizing, economic analysis, scheduling as well as debottlenecking of different processes (Oke et al., 2017; Lee et al., 2020; Oke et al., 2021; Adeyi et al., 2021; Okolie et al., 2021). Application of process simulators speeds up product/process development and also shortens process cycle times, this also reduces experimental burdens of the entire process which could have been financially demanding. Therefore, to transform petroleum wastewater into an economically profitable treated water using the integrated filtration process in BFS system, basic process engineering tools and techniques are required. Thus, this investigation was performed in order to investigate the feasibility of integrating filtration into bio-clarified water production from petroleum wastewater via computer-aided design, techno-economic evaluation and Monte Carlo uncertainty analyses of the process.

Process design feasibilities are often analyzed by statistical sensitivity of process parameters uncertainties which consider the viability of the techno-economic model. Uncertainty analysis indicates the extent of the degree of associated uncertainty or risk involved in making the decision on the forecasting performance indices of the process design. Oke et al. (2021) used Monte Carlo simulation to carry out sensitivity and uncertainty analyses on the effect of process input parameter variance on the regression model. It was reported that the negligible uncertainty value observed in the study depicted the degree of the reliability and prediction of the model. Similarly, Monte Carlo uncertainty analysis was performed by Adeyi et al., 2021 in order to investigate the degree of predictability of black box model that predicted the effect of various alkali treatment of ampelocissus cavicaulis fiber on the tensile property of reinforced polyester composite. For further clarity, Monte Carlo simulation is a probability based computerized stochastic technique required in the generation of random variables for modelling sensitivity and uncertainty associated with certain systems (Chaves et al., 2016; Oke et al., 2017; Oke et al., 2020a).


Biocoagulation-flocculation-sedimentation (BFS) of petroleum wastewater is one of the preferred green primary wastewater treatment technology used for turbidity removal.  Studies on petroleum-produced effluent BFS provides valuable laboratory experimental details on biocoagulants properties and process operating conditions needed for understanding of BFS system dynamics.

Works on bio-clarified water production, process kinetics, optimization, mechanistic and black box modelling are well reported by Menkiti et al., 2011; Nnaji et al., 2014; Oke et al., 2018; Menkiti et al. 2016; Ugonabo et al., 2020; Okolo et al., 2016 and Menkiti et al., 2017 but, conceptual proof-of-concept, process scale-up simulation modelling, process design and integration of Filtration into BFS of PPW for possible process development for the future commercialization have not been documented in the pool of scientific literature. Also, thorough reviews of literature have revealed that there are no published articles on the computer – aided process integration, economic evaluation and Monte Carlo uncertainty analysis for bioclarified water recovery from petroleum wastewater. Hence, this investigation is lengthening the previously published experimental work of Menkiti et al. (2016) so as to bridge the established gab. This study is therefore the first innovative research that investigated the techno-economic feasibility of bio-clarified water reclamation from petroleum wastewater using ASPEN batch process developer and Monte-Carlo simulation. Therefore, the major focus of this investigation is to bridge the research gap found in the existing literature by developing base case scale-up simulation model for petroleum wastewater bio-clarification using laboratory experimental data, economic-profitability analysis and uncertainty quantification model for bio-clarified water production from petroleum wastewater. This research is focussed on developing a computer – aided process integration, economic evaluation and uncertainty analysis for bioclarified water production from petroleum wastewater.

1.3       AIM OF STUDY

The aim of this work is to develop a computer – aided process integration, economic evaluation and uncertainty analysis for bioclarified water production from petroleum waste water

1.4       OBJECTIVES

1.        To develop scale–up base case simulation model for petroleum wastewater bio-clarification using laboratory experimental data and to integrate filtration process into the simulation model.

2.        To perform economic evaluation for the integrated bioclarified water production from petroleum wastewater.

3.        To investigate the effect of techno-economic parameters (fixed capital investment, direct production cost, indirect production cost, selling price and annual capacity) on profitability indices via Response Surface Methodology

4.        To perform Monte Carlo simulation uncertainty and sensitivity on the simulation model


This study is the first innovative research that has investigated the techno-economic feasibility of bio-clarified water reclamation from petroleum wastewater using ASPEN batch process developer and Monte-Carlo simulation. Therefore, the major focus of this investigation is to bridge the research gap found in the existing literature by developing base case scale-up simulation model for petroleum wastewater bio-clarification using laboratory experimental data, economic-profitability analysis and uncertainty quantification model for bio-clarified water production from petroleum wastewater.


The modelling and simulation of the base case of petroleum wastewater bio-clarification using laboratory experimental data and integration of filtration process into the bioclarification process form the scope of this research work. Economic evaluation, taking into consideration the effect of techno-economic parameters (fixed capital investment, direct production cost, indirect production cost, selling price and annual capacity) on profitability indices via Response Surface Methodology also form part of the scope. This also includes: uncertainty and sensitivity analyses on the simulation model via Monte Carlo simulation.

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