Refinery Water

Process Control

DEPLOYMENT OF BIOKINETIC MODELING TOOLS TO OPTIMIZE REAL TIME BIOLOGICAL WASTEWATER PROCESS CONTROL IN THE OIL REFINING INDUSTRY 
 
TAKING BIOKINETIC MODELING FROM THE ENGINEERING FIRM TO THE REFINERY’S WASTEWATER PLANT CONTROL ROOM
Perhaps no industry has more operationally failing wastewater treatment plants than those deployed in the Oil & Petrochemical market segments. These plants have some of the toughest influent contaminant profiles to treat, and suffer from some of the largest ranges of variation in contaminant loadings. The toughest area of wastewater process control in Oil Refining of course lies in the Biological Treatment areas, due to the vast number of process control variables when compared to the neighboring chemical processes. Industry specific operating conditions further aggravate these plants’ process control efforts, such as:
1.    The presence of recalcitrant Aromatics, PNAs, and PAHs.
2.    The presence of a wide variety of Nitrification inhibitors.
3.    Eight or even more independent sources of influent, all with varying characteristics, coming from oil processing units whose personnel rarely interact with each other, nor with the wastewater treatment plant operational staff.
4.    Lack of sufficient early warning resources to detect loading changes or inhibitory toxics.
5.    Lack of sufficient Equalization.
6.    Insufficient Diversionary 
7.    Operation in upset mode more of the time than in design mode.
8.    Operation in Dynamic State more often than in Steady State.
To make matters worse, most refinery wastewater plants are not designed empirically, but rather are based on the theoretical design data from someone else's wastewater plant, under steady state conditions which of course is only part of the time in an Oil Refinery. Under the conditions that a refinery biological wastewater plant has to operate under, these assumptions more often than not fail in the real world of process control. For example, to really control the Activated Sludge process with the most current MCRT methodology, you have to establish the optimum MCRT range within which to operate for the realization of the specific effluent treatment goals. How do you do this without the Biokinetic Constants and Coefficients for the specific plant design and the specific range of influent profiles, unless you're willing to take an educated guess?? Historically, these guesses work well in some industries, but in oil refining, these guesses have more often been the number one cause of the inability to control the biological treatment process, which of course culminates in effluem permit excursions. With the Biokinetic Constants, you can precisely and mathematically determine the MCRT control parameters, and all related real time process control adjustments. Moreover, with the tremendous number of variables in a Biological Wastewater Treatment system, it is difficult in the extreme to quantitatively interpret the results observed from experimental or unintentional operational excursions from the normal control ranges. The use of Biokinetic Models as a measurement tool provides the last word in an absolute metric format upon which the success or failure of an operational change can finally be based, without questions raised as to potential interferences in interpretation. And last but not least, without the actual field determination of the specific Biokinetic Constants for a given plant, many attempts to operate a true MCRT Control Program fall short, such that the end result resembles more of a Food-To-Mass (F:M) Control Program based on Trial and Error operational strategy with its considerable built-in error based on analytical methods available for the measurement of the food. This error is more than significant in oil refinery wastewater due to the dependence of the F:M calculations upon BOD and its nonlinear relationship to COD or other quick test substitute parameters.
 
MEAN CELL RETENTION TIME (MCRT) VS. FOOD TO MASS (F:M) PROCESS CONTROL STRATEGY
In some wastewater applications, the use of the F:M strategy for control of the Activated Sludge process works well. However, in many types of industrial settings, perhaps none more notable than Oil Refining, this strategy falls way short of adequate. This is due to:
  1. Wide ranges of relative biodegradability of the substrate (Food) in the influent.
  2. Wide ranges of variability in the influent.
  3. The intermittent presence of biologically toxic and inhibitory compounds in the influent.
Inherently, the actual calculation of F:M has several pitfalls:
  1. In Oil Refinery Wastewater, there is no representative quick test for the substrate. BOD5 would be representative, but does not meet quick adjustment turnaround times. COD, TOC, and TPH do not have a consistent linear relationship to BOD5 in refinery wastewater. As such, considerable error in process control enters right in the mere calculation itself. Conversely, the use of the MCRT strategy does not depend on measuring the substrate.
  2. Unlike the use of the Mean Cell Retention Time (MCRT) strategy, F:M cannot be directly related mathematically to the microbial growth rates. As such, most of the operational and process control benefits of Biokinetic Modeling cannot be effectively achieved with F:M. Only MCRT can capture the entire spectrum of benefits which translate in operational cost savings.
  3. Unlike the MCRT strategy, the process for determination of the optimum target control ranges for F:M is not practical under the conditions that oil refinery AS processes operate. As such, the optimum target F:M ranges are usually based on some other plant’s design and characteristics, which usually do not match the specific plant’s process considerations.
  4. Adjustment of Sludge Wasting Rates to control the F:M Ratio is a Trial and Error process. With the use of the MCRT strategy, Sludge Wasting is calculated precisely and administered mathematically to hit the target control range.

 

MORE MILES PER BUG

The use of Biological Treatment has a narrow fit in the overall scheme of available technologies applicable for processing wastewater and related sludges. However, when Biotechnology does fit, there is no alternative more cost effective. Within the range of various Biological Treatment designs, there is no process more efficient and more controllable than the Activated Sludge (AS) process. Based on this premise, a worthwhile goal of refining wastewater plants would be to a initiate a path directed toward maximizing the return on investment into the AS system. In other words, making a long term concerted effort toward trying to have the AS System consume as much of the refinery’s wastes as possible. But, how much can the AS System take? What is its true operational capacity based on what really comes down the sewer? How do you determine what the limit really is?Although Biological Treatment has proven its effectiveness, trying to determine the parameters for optimizing its performance is not easy. Because of the tremendous number of process variables involved in Biological Wastewater Treatment, performance and control are not always straightforward. In many cases, metrics deployed to attempt to make these determinations appear indicative on a Macro View level. But, because there are many other Non-Biological Chemical Mechanisms occurring simultaneously in a Bioreactor, on a Micro View level what may appear to be the result of Biological Treatment may in fact be facilitated by enzymatic reactions, chemical oxidation, precipitation, adsorption, ligand complex formation reactions, sludge entrapment, air stripping, and more. Now add to that problem the fact that especially with Oil Refinery Wastewater, steady state conditions are not always prevalent, and in addition, the text book stoichiometric biomass relationships are frequently skewed by the presence of inhibitory toxic compounds.   In short, when attempting to quantitatively define all of the important metrics related to gauging the true performance of the specific Microbiological Population functioning in a given plant, there is only one way to accomplish that with 100% reliability, and that is through the use of Biokinetic Modeling Tools. Only if you know the true kinetic and metabolic reactions of microbial growth in a system, are you able to truly control that process. And this knowledge culminates in maximizing the true operational plant capacity, starting with maximization at the individual microbial cell level. 

 
USING BENCH SCALE SIMULATION TO FINE TUNE BIOKINETIC MODELING
Lab Bench Scale Continuous Feed Process Simulation in multiple application areas throughout a Refinery is a wave of the future. The key indicator for reaping an operational benefit with this tool falls in target areas where the Refinery has little room to play with process control variables due to the sensitive nature of the effluent quality, compliance risks, or effects on the production process itself. In other words, they cannot fully exploit process optimization too far from the middle of the established control ranges due to critical restraints. Furthermore, rarely will the sole train of a full scale plant be allowed to play with changes in more than one variable at one time. Unfortunately, the relationships of many Biological Treatment variables are in fact influenced by Multiple Correlation phenomenon. Sometimes the productive operational adjustments do lie at the outer ranges of standard deviations from the normal ranges. And more importantly, in utilizing modern mathematical tools to the fullest extent, the evaluation of a plant’s performance variables at the higher ranges of standard deviation are vastly valuable in fine-tuning the accuracy of the model itself. Most Refineries do not have the budget to construct processes which have standby units which could be alternately used for experimentation. The use of a simple to build and easy to operate Online Bench Scale Simulator of the full scale AS system would solve the aforementioned problems and add greatly to the accuracy, effective predictability, and overall value of the concurrent Modeling efforts. 
 
HOW DO YOU CONDUCT A FIELD BIOKINETIC MODELING STUDY?
Using chemical engineering principles, bioengineers have quantitatively created reaction rate mathematical models that have primarily been used for design and sizing calculations.   Similar design models can be constructed based upon real world observation and collection of material balance and substrate balances across an operating activated sludge plant.  This is predicated upon the proper analytical program to collect mass and volumetric flow data along with the corresponding chemical analyses. This is not a trivial task in the confines of an operating unit wastewater operation compared to a controlled laboratory environment, but it can be achieved.   
·         Determine the Biokinetic Constants and Variables including:
1.      è = Mean Cell Retention Time.
2.      1 / è =  Growth Rate of Microbial Population.
3.      Y  =  Cell Yield.
4.      U  =  Specific Substrate Utilization Rate.
5.      Kd  =  Decay Rate Coefficient.
6.      K  =  Specific Substrate Utilization Coefficient.
7.      k  =  Maximum Substrate Utilization Rate.
8.      Ks  =  Half Saturation Constant (Effluent ---> ½ k).
The underlying biological kinetic expressions can be obtained from fitting operating data as a function of microbial growth rate.   The data can be fitted using computerized linear regression analyses which ensures the predictability of the model.
·         Build and calibrate the plant specific Biokinetic Model and related Equations, including:
1.      Target MCRT Model vs. Effluent Quality.
2.      Construct a Predictive “What If” Model that will allow you to manipulate changes in influent flow and quality.
3.      Assess the extent of inhibition biokinetic response and adjust the model accordingly.
4.      Determine optimum steady state operating conditions and determine operating strategy during non-steady state conditions.
5.      Calculative Model for Process Variable Adjustments. 
Once the Biokinetic constants are determined, a comprehensive model across all normal and transient operating conditions can be predicted.  Predictive control of complex biological systems is the next stage to maximum utilization of refinery treatment assets as well as maintaining effluent quality. Just as chemical reaction kinetics are used in every upstream refinery unit operation to optimize the process, biological kinetic modeling can be utilized to achieve a greater control over environmental stewardship as wells as return on investment.  
 
WHAT IS THE OPERATIONAL SIGNIFICANCE OF THE BIOKINETIC CONSTANTS?
The key operational Biokinetic Constants for real time process control considerations would be: 
  • Maximum Substrate Utilization Rate (k) – This number defines the total contaminant loading capacity of the entire Biological Plant, and can be calculated in terms of Organic (Carbonaceous) mass or Nitrogenous (Autotrophic) mass. 
  • Cell Yield (Y) – This number defines the Biomass Production and Carbon Dioxide (CO2) Generation resulting from Biological Oxidation, and can be expanded to define the equilibrium shift between the CO2 and the produced Biomass. The ability to measure this enables a plant to control and shift the equilibrium, thus placing a handle on such important factors as Sludge Disposal and Oxygen Utilization.

The key operational Biokinetic equations for plant performance evaluations and “what if” simulations would be:

  •  1 / è  =  (Y) (U)  -  Kd                        [Growth Rate vs. Substrate Utilization]
  • Se  =  (1 / è  +  Kd)  /  (Y) (K)           [Effluent Substrate vs. MCRT]
  • Y  =  (1 / è  +  Kd)  /  (K) (Se)           [Biomass Generation vs. CO2]
 
CASE HISTORY EXAMPLE OF THE SIGNIFICANCE OF BIOKINETIC MODEL DETERMINATIONS
Below are the actual results obtained from a large U.S. Oil Refinery Wastewater Plant Biokinetic Modeling project. In this case, the refinery experimented with a process change which represented a controlled change in the metabolic activity of carbonaceous microorganisms deployed in the Activated Sludge Aeration Basin, at the cellular level. The “Before” column represents operation under normal historical conditions..The “After” column represents operation during the experiment. 
Biokinetic Constant Before After
     
k (Maximum Substrate Utilization Rate) 0.469 mg COD / mg SS-day 0.935 mg COD / mg SS-day
Ks (Half Saturation Constant) 140.61 mg/L COD 555.56 mg/L COD
K (Specific Substrate Utilization Coefficient) 0.0033 L/mg COD day 0.0017 L/mg COD day
Y (Cell Yield) 0.25 mg SS / mg COD 0.33 mg SS / mg COD
Kd (Decay Coefficient) 0.035 days-1 0.030 days-1
So what significant knowledge was gained with the study results obtained above?
 
  1. The Maximum Substrate Utilization Rate increase demonstrated that the plant nearly doubled its total Organic Loading Capacity by employing the operational changes during the experiment.
  2. The Cell Yield increase demonstrated that the Biomass Production increased nearly 30% by employing the operational changes during the experiment.

So what did the plant conclude about its future prospective alternatives? They learned that by deploying the experimental conditions permanently, they could gain a 50% increase in plant capacity, but at a cost of a 30% increase in Biomass disposal costs. 

If we could leave you with one concluding concept, it would be this: 
Biokinetic Modeling + Source Control + Bench Scale Continuous Plant Simulator
= State of the Art Oil Refinery Wastewater Process Control
 

The article above is a reprint from several major Water & Oil Industry Trade Magazines. For more detailed information on the processes described in the Technical Paper, please contact:

Refinery Water Engineering & Associates  

dk@refinerywater.net