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Renewable Energy

RENAISSANCE

Real-time characterization of material flows for optimal operation of combined heat and powerplants, wastewater treatment plants and waste management facilities and sustainable use of resources within circular economy.

Start

2021-04-01

Planned completion

2022-09-30

Research area

Project manager at MDH

No partial template found

Today's world is characterized by ever-growing energy and material demands resulting in production of large amounts of wastes in various forms. A sustainable waste management approach is required to address this environmental threat. According to the European waste management hierarchy waste re-use, recycle and energy recovery is strongly preferred over waste disposal. Components of the waste such as plastics can be sorted out and reused or recycled to increase their useful lifetime. To assist waste recycling and energy recovery, technologies that classify waste components for automated on-line sorting are required. Moreover, biomass originating from forest residues as well as from other sources is material with highly variable properties that makes its utilization in energy conversion processes complex as it creates undesired process instabilities. Therefore, there is a need for sensors that can measure the properties of interest or classify materials in real-time to optimize process operational and regulatory measures.

Furthermore, Contaminants of Emerging Concerns (CECs), such as pharmaceutical in the wastewater is of critical importance because of their potentially harmful impacts on environmental resources and exposure to humans and biota. While many of the CECs have been present in the environment for decades, the rising concern is being driven by the importance of analytical techniques that are able to detect them rapidly and at low concentrations. Today, there is a need to establish a protocol for detection of these CECs in the wastewater as well as the sludge. Analysis of CEC in wastewater may be challenging, dealing with various particle sizes, structures, shapes and polymer types dispersed in complex environmental matrices. However, due to the increased presence of CECs in the environment, it is of great importance to start evaluating new analytical techniques.

The RENAISSANCE project aims to enable real-time characterization of municipal solid waste components as well as biomass, sludge and measurements of wastewater properties of interest using machine learning and artificial intelligence (AI) algorithms. The important properties will be identified within the project and may include e.g. elemental composition, classification of waste components to enable waste sorting, various CEC concentration in wastewater and sludge, other QA/QC indicators etc.

Primary purpose

Demonstrate the potential of implementing optical spectroscopy sensors coupled with artificial intelligence (AI) based data analytics for real-time qualitative and quantitative characterization of properties of interest in important material flows for optimal operation of combined heat and powerplants (CHPs), wastewater treatment plants (WWTP) and waste management facilities and optimal use of resources within circular economy.

Project objectives

1. Identify appropriate material flows (e.g. samples of waste, biomass, sludge and wastewater etc.) in CHPs and WWTPs and important qualitative and quantitative properties of interest (e.g. elemental composition, energy content, prediction of thermochemical behavior, various important CEC concentrations, other QA/QC indicators, classification/discrimination of components etc.) based on coproduction with industrial partners and literature review.

2. Identify (a) the most appropriate reference chemical analysis method and (b) spectroscopy technique for qualitative and quantitative characterization to enable real-time measurements. Perform sampling, sample preparation and acquisition of spectroscopy data as well as reference data → Sampling and data acquisition protocol.

3. Identify and employ appropriate spectral preprocessing techniques and develop multivariate regression and classification models using machine learning and artificial intelligence algorithms that enable real-time measurements and evaluate model performance for defined material/water properties of interest → Multivariate modeling protocol.

4. Evaluate potentials and limitations of the developed methodology to enable real-time characterization of important properties of interest in material flows and to propose its implementation strategy for optimal operation of CHPs and WWTPs production systems and waste management facilities and optimal use of resources within circular economy.


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