Researchers Develop Decision Support System to Help Prevent Plant Upsets

Whether caused by unauthorized discharge, accidental spill, or terrorist activity the presence of toxic compounds in the influent to a wastewater treatment plant is an obvious cause for concern for the plant manager and staff, as it can cause serious upsets to biological treatment processes. Research from two WERF projects provides valuable information that will enable wastewater treatment plants to develop a decision support system (DSS) that could reduce or potentially eliminate the impact of suspected toxic compounds on the facility.

The first project, Feasibility Testing of Support Systems to Prevent Upsets (03CTS7S), was led by Andrew Shaw of Black & Veatch. Shaw and his team developed a DSS prototype based on  the study of techniques used in other industries, as well as real word accounts from more than a hundred treatment facilities throughout the United States. Determining and Assessing Corrective Action Strategies for Treatment Plants Exposed to Chemical Toxins (04CTS11S), which was led by Nancy Love, Ph.D., of the University of Michigan, built on the lessons learned in that earlier project with the development and testing of a DSS framework that wastewater treatment plants can use to develop a site-specific system to select and carry out corrective actions.

Laying the Groundwork

Research for Feasibility Testing of Support Systems to Prevent Upsets began with the collection of background information on decision support systems (DSS), including a comprehensive review of software and data analysis techniques used by various industries to detect upsets. The review served as the basis for an assessment of techniques most appropriate to the wastewater industry and suitable for inclusion in an electronic DSS that can link to a plant’s supervisory control and data acquisition (SCADA) system to help operators  prevent plant upsets. In the process, the research team identified four key features for a good DSS designed to prevent plant upsets:

• The DSS should detect anomalies or unusual activity as early as possible.
• Various anomaly detection methods are available, and all of them require a database of historical data, which plants can use as the basis for detecting unusual conditions.
• A case-based reasoning system (CBRS) is an approach to computer-based learning from the field of artificial intelligence (AI) that can provide a useful approach for a DSS. A CBRS uses cases, which are examples of past problems encountered and solved. Cases are organized in a database called a case base. A CBRS looks for patterns in the attributes of the current case and compares them to those for cases in the case base.
• Many online instruments and analyzers are available that can be used to provide data for the DSS. The development of these devices is moving forward rapidly.

In addition to the review, the research team conducted a survey of more than 100 wastewater treatment facilities, mostly in the United States, to see how many of them experienced upsets, what they thought caused the upsets, and the approaches they took to mitigate the impacts. The most common cause of upsets was toxic organic shock loads (i.e. a toxic substance in the influent caused a plant upset), which frequently resulted in ineffective nitrification, deflocculation of the activated sludge floc, or foaming. The most commonly used methods for upset mitigation were influent diversion (i.e. diverting influent flows to a holding tank) and “source determination and termination” (i.e. finding out where the toxic substance came from and taking action to prevent it from happening again).

Having established baseline information about plant upsets and potential DSS elements, the project went a step further by developing a DSS prototype using online analyzers and a separate array of sensors in the primary effluent of a wastewater treatment plant (hereinafter Plant 1). These instruments provided data to which data mining techniques were applied in order to test for anomalies that would indicate the presence of known toxins added in the test rig. The online analyzers included two respirometers and a spectrophotometer. Sensors in the separate array included pH, oxidation-reduction potential (ORP), dissolved oxygen (DO), temperature and conductivity. Clustering of the data from the pH, ORP, and conductivity provided very promising results for many of the tested contaminants. The research team identified several practical issues through the prototype testing which they believed should be addressed in a full-scale DSS:

Automated real-time data from sensors located as near to the plant inlet (or in the sewer system) provide the best means for being able to initiate action to prevent plant upsets; however, it is difficult to maintain sensors and analyzers in the high-fouling environment in these locations.

• Maintenance of primary sensors to provide consistent data is imperative for anomaly detection.
• Data filtering is required to enable the DSS to distinguish between anomalies and other routine activities such as sensor maintenance and sensor faults.
• The DSS needs to collect, analyze, and store large amounts of data.
• Automation can provide rapid information for a DSS, but it is important to include humans in the ultimate decision making process for carrying out corrective actions.

Many of these issues were addressed in the follow-on project Determining and Assessing Corrective Action Strategies for Treatment Plants Exposed to Chemical Toxins (04CTS11S), in which attention was given to the corrective actions that follow the alarm raised by the DSS.

From Prototype to Proving Ground

Determining and Assessing Corrective Action Strategies for Treatment Plants Exposed to Chemical Toxins commenced with a workshop involving operators, managers, consultants, and researchers to brainstorm ideas for corrective action strategies to mitigate the potential impact of toxins if they are detected by the DSS and to identify which strategies they thought would be most effective.

Two very different facilities were used as case sites in order to give different perspectives for the corrective actions that should be taken for each. Neither plant had previously established corrective action strategies for the type of toxic events being considered for the DSS. Plant 1 is an activated sludge facility with two separate treatment trains that provide basic primary and secondary treatment with BOD removal only and seasonal nitrification. A second WWTP (Plant 2) provides more advanced treatment with full biological nitrogen and phosphorus removal followed by tertiary filtration. The research team selected different corrective actions specific to the different facilities.

After the workshop, researchers set up lab-scale test rigs to simulate the two different facilities and different corrective action strategies were tested. Following this initial work, researchers constructed a pilot-scale facility of Plant 1. This rig was equipped with multiple online sensors throughout the system, and researchers simulated toxic events using hypochlorite, which is a powerful oxidant that stays mainly in the liquid phase, and then cadmium, which is a toxin that mainly accumulates in sludge. The team tested these corrective action strategies:

• Sludge diversion and storage while event takes place, in order to protect the activated sludge during the toxic event and thus enabling it to return quickly to full treatment following the event.
• Purposely short-circuiting aeration basins by shutting off aerators and allowing the mixed liquor to settle, while the bulk of the flow passes over the activated sludge, hopefully preventing it from seeing significant toxin concentrations.
• Chemically Enhanced Primary Treatment (CEPT) to remove toxins in the primary tanks, thus preventing them from getting to the secondary treatment stage where they would cause significant upsets to biological treatment.

A final output from Determining and Assessing Corrective Action Strategies for Treatment Plants Exposed to Chemical Toxins (04CTS11s) is a DSS framework that plants can use to develop a site-specific system to select and carry out corrective actions. The DSS framework provides a practical basis for any facility to develop its own DSS through consideration of the following six stages:

• Stage I: Receive Warning, either through an automated system linked to online sensors, a pre-warning via a telephone call, or plant operators noticing some problem on site.
• Stage II: Gather Additional Information to determine if the contaminant is likely to cause an upset.
• Stage III: Characterize Contaminant to determine the expected fate and type of impact of the contaminant and decide which corrective action should be used.
• Stage IV: Take Immediate Corrective Action based on the information from previous stages.
• Stage V: Monitor to determine the effectiveness of the corrective action and potentially make adjustments.
• Stage VI: Take Longer Term Action (Recovery) to bring plant back into normal operation.  Also, feedback is given from the last two stages to enable the DSS to be constantly improved.

Wastewater treatment plants need proactive corrective action strategies that minimize the impact of chemical, biological, and radiological contaminants on wastewater treatment plants.  This DSS framework provides operators with a systematic decision making process in which to respond to a range of contamination events that will minimize the degree to which damage is imparted to the treatment process and environment, while ensuring the utility’s ability to meet their obligations to regulators. In addition, the pilot study data and a broad range of background information generated by the two projects are invaluable to any utility looking to develop their own DSS.

This research shows that it is feasible to develop support systems to prevent upsets using real-time information collected by sensors/analyzers subject to advanced data mining techniques to predict the experimentally simulated toxic shock, leading to more proactive approaches to minimize impacts and assisting in the execution of remedial actions following the detection of an upset event.

For more information, please contact Claudio Ternieden, WERF’s assistant research director, at cternieden@werf.org. More information on WERF’s security-related research efforts can be found online at www.werf.org/security.

WERF thanks Andrew Shaw, process specialist for Black & Veatch, for contributing this story.

December 3, 2009