Wastewater Dosing Control With Multi-Level Fallback Logic
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Solution Overview
Problem
Current wastewater treatment systems for drilling operations, especially after fracturing, lack an efficient control mechanism to manage disruptions and ensure continuous operation, often resulting in downtime and increased costs due to the need for fresh water conservation and effective treatment.
Innovation Solution
A multi-level control system for wastewater treatment that automatically reverts to a lower level of control upon disruptions, utilizing volumetric dosing, Key Performance Indicators (KPIs), Bayesian methods, machine learning, and an aliquot array assay to optimize chemical addition and treatment, ensuring continuous operation and cost-effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a single-level control system is used for wastewater treatment, then the system is simpler to operate, but it cannot adapt to disruptions and results in downtime
Solution Approach 1:
The control system is divided into multiple hierarchical levels (Level 1: Base volumetric dosing control, Level 2: KPI-based optimization control, Level 3: Advanced machine learning control). Each level operates independently with defined functionalities, allowing the system to segment control tasks across different complexity tiers and select appropriate levels based on operational conditions and disruption scenarios.
Solution Approach 2:
The control system dynamically transitions between different levels based on real-time operational conditions, sensor data availability, and disruption detection. The system can revert from higher levels to lower levels when disruptions occur, and escalate back when conditions improve, providing adaptive responsiveness without requiring permanent complex infrastructure.
2Productivity
If advanced control methods (machine learning, Bayesian methods) are implemented, then treatment optimization improves, but system complexity and cost increase
Solution Approach 1:
The system pre-configures multiple control levels with predetermined algorithms and methodologies (volumetric dosing, KPI-based control, machine learning models) before operational needs arise. This allows advanced control capabilities to be readily deployed when beneficial while maintaining the option to revert to simpler methods, avoiding permanent complexity investment.
Solution Approach 2:
The control system is designed with multi-functional capability where a single integrated platform can execute multiple control strategies (from simple volumetric dosing to complex machine learning) depending on the operational context. This universal design allows the system to adapt its complexity level to match the required treatment optimization without requiring separate dedicated systems for each control method.
3Reliability
If the system reverts to lower control levels during disruptions, then continuous operation is maintained, but chemical optimization is reduced
Solution Approach 1:
The system prepares fallback control levels (Level 1 volumetric dosing, Level 2 KPI-based control) in advance that can be activated when disruptions occur at higher levels. These pre-configured backup levels ensure continuous operation is maintained during disruptions, with Level 1 providing basic reliable dosing and Level 2 offering optimized dosing when sensor data is available, cushioning against the loss of advanced control capabilities.
Data Source
AI summary
A system and method for process control using multiple levels of control is disclosed. The system utilizes a first level of control which provides a dosing regimen based solely on volume. A second level uses predictive analysis or other such tools to predict a dosing regimen. A third level uses an array of small scale testing to test various dosing regimens to determine which one is optimal. If a disruption event occurs for the second or third level, or the system is off-line, the system reverts back to the first level control.