the same time, drinking water systems have increased in complexity and feature
smarter elements, which in turn leads to a data-richer operational environment.
Castro-Gama et al. [3] address this challenging context and the often-overlooked
factor of ensuring high data quality and preventing errors in data streams. The
chapter provides a bird’s eye view of data validation in the drinking water industry
of The Netherlands towards better data quality control policies, by providing
insights on (raw) data validation in two problem types, one of water quantity and
one of water quality. The chapter concentrates on a specific aspect of the overall
data quality control chain, which deals with faulty data detection and isolation.
Furthermore, of interest here are errors in the measurements, because sensing and
human data editing processes lead to raw data distortion in the form of, e.g., drift,
bias, precision degradation, or sensor failure. The focus lies on data validation to
determine faulty data and the identification techniques, without expanding further
on the decision-making process regarding accepting or rejecting faulty data. The
authors present the results of surveys conducted with four water companies, a
literature review on faulty data detection techniques, and then propose a data
quality control approach using simple techniques. Case study results are presented
including data validation for one water company. Best practices and issues arising
from these examples regarding data quality control by water utilities are identified,
as well as recommendations for future research and application of faulty detection
techniques in the Dutch drinking water sector.
Monitoring wastewater has always been a challenge. Wastewater systems can
vary in both size and complexity ranging from small and simple rural catchments to
large and complex urban conurbations. In the wastewater collection network,
historically, there has been a lack of permanent wastewater monitoring because
of the propensity for fouling and the complications of monitoring both gravity and
pressurised networks. In engineering and operational terms, the wastewater network has also been treated as a separate entity to the wastewater treatment works,
which is, in reality, part of the same system. The wastewater treatment works tend
to be much better monitored depending upon the size of the works. However, this
monitoring has been very much based upon single system instrument-based control
systems (e.g., a dissolved oxygen control system for an activated sludge plant).
Grievson [4] presents a more holistic systematic approach, which is based upon the
philosophy of the resource factory and treating the outputs from the wastewater
treatment works as a product. The chapter looks at the different elements of the
system as a whole and looks at the philosophy of operation that a smart system
would put in place and the measurement and control needs required. The future of
both the wastewater network and the wastewater treatment works will be a much
more holistic approach bringing the network and the treatment works together and
treating it as a single system. In this way, rather than operating the wastewater
treatment system for process control with the aim of protecting the water environment, it can also be operated for resource recovery and energy efficiency with a
much wider environmental benefit. This chapter provides valuable background for
researchers and practitioners interested in smart wastewater networks (including
Preface
xi
smarter elements, which in turn leads to a data-richer operational environment.
Castro-Gama et al. [3] address this challenging context and the often-overlooked
factor of ensuring high data quality and preventing errors in data streams. The
chapter provides a bird’s eye view of data validation in the drinking water industry
of The Netherlands towards better data quality control policies, by providing
insights on (raw) data validation in two problem types, one of water quantity and
one of water quality. The chapter concentrates on a specific aspect of the overall
data quality control chain, which deals with faulty data detection and isolation.
Furthermore, of interest here are errors in the measurements, because sensing and
human data editing processes lead to raw data distortion in the form of, e.g., drift,
bias, precision degradation, or sensor failure. The focus lies on data validation to
determine faulty data and the identification techniques, without expanding further
on the decision-making process regarding accepting or rejecting faulty data. The
authors present the results of surveys conducted with four water companies, a
literature review on faulty data detection techniques, and then propose a data
quality control approach using simple techniques. Case study results are presented
including data validation for one water company. Best practices and issues arising
from these examples regarding data quality control by water utilities are identified,
as well as recommendations for future research and application of faulty detection
techniques in the Dutch drinking water sector.
Monitoring wastewater has always been a challenge. Wastewater systems can
vary in both size and complexity ranging from small and simple rural catchments to
large and complex urban conurbations. In the wastewater collection network,
historically, there has been a lack of permanent wastewater monitoring because
of the propensity for fouling and the complications of monitoring both gravity and
pressurised networks. In engineering and operational terms, the wastewater network has also been treated as a separate entity to the wastewater treatment works,
which is, in reality, part of the same system. The wastewater treatment works tend
to be much better monitored depending upon the size of the works. However, this
monitoring has been very much based upon single system instrument-based control
systems (e.g., a dissolved oxygen control system for an activated sludge plant).
Grievson [4] presents a more holistic systematic approach, which is based upon the
philosophy of the resource factory and treating the outputs from the wastewater
treatment works as a product. The chapter looks at the different elements of the
system as a whole and looks at the philosophy of operation that a smart system
would put in place and the measurement and control needs required. The future of
both the wastewater network and the wastewater treatment works will be a much
more holistic approach bringing the network and the treatment works together and
treating it as a single system. In this way, rather than operating the wastewater
treatment system for process control with the aim of protecting the water environment, it can also be operated for resource recovery and energy efficiency with a
much wider environmental benefit. This chapter provides valuable background for
researchers and practitioners interested in smart wastewater networks (including
Preface
xi