Wasteomics ⇒ a workflow to analyze complex reaction environments, waste, and realistic feeds conversions


Analytical workflow (ANW)

Wasteomics: a quantitative, molecular-level workflow for complex reaction media, waste, and realistic feeds. We describe the complete reaction medium at the molecular level, using realistic feeds instead of model compounds, and we convert that description into reaction networks and kinetics. The workflow covers the gas, the liquid, and the solid, including the species that remain trapped on the catalyst.

Problem statement. In heterogeneous catalysis, the reaction medium is a mixture of reactants, intermediates,s and products. Part of it is adsorbed or trapped on the catalyst and elsewhere in the reactor. These media are multiphase, contain unstable species, es and are difficult to analyze. Some species also act as catalysts or as deactivating agents for the conversion of others.

The usual practice is to study model molecules or surrogates to resolve mechanisms, microkinetics, or spectroscopy. A model compound, even an abundant one, does not reproduce the kinetics of the mixture [110] . Analytical techniques keep improving, but each one targets a fraction or a type of species. No single technique resolves the whole mixture.

The gap is largest for the feeds that a circular economy depends on: plastic waste, tires, sewage sludge, biomass, algae and seaweed, and refinery streams of low value.

Our approach, scale by scale. The -omics fields inspire our approach: we characterize and quantify the full pool of molecules and relate it to structure, function,n and dynamics. The video follows one sample from the process to the reaction network, and each paragraph below matches one scene.

1. Process: real feeds. We work with the feeds as they are. These include waste polyolefins [86] , scrap tires [87, 98, 107] , oxygenated plastics such as PMMA and PET [143, 172] , marine heavy-fuel waste oils [123] , crude oil [125] , bio-oils from biomass [99, 100, 186] and algae [193] . Each one is a mixture of thousands of molecules. We study two upcycling routes:

  • co-feeding waste-derived streams into existing refinery units such as FCC and hydroprocessing, using their know-how and infrastructure [86, 98, 194] ;
  • new processes for sustainable, bio- or waste-refineries.

2. Reactor: every phase is sampled. We sample every phase that leaves the reactor: the gas, the liquid, and the spent catalyst. The spent catalyst holds the trapped and deactivating species, which are usually left out of the mass balance [138, 196] .

3. Analysis: several techniques, one dataset. No single instrument reads the whole sample. We combine high-resolution mass spectrometry with chromatographic, spectroscopic,c and thermal techniques, and we merge their results into one quantitative dataset per experiment [186, 194] . The same protocols apply to the coke extracted from the catalyst [138] .

4. Molecules: the mixture is mapped, not averaged. Each signal becomes a molecule with formula, class, s, s, and concentration. This molecular map shows where heteroatoms (O, N, S) go during upgrading [125, 172] and how coke precursors form and grow on zeolites with time on stream [109, 138, 196] . In some cases, coke is not only a problem: on a hierarchical zeolite, partial coking blocks the sites that drive side reactions [122] .

5. Network: from molecular data to kinetics. Molecules and lumps are linked into reaction networks and kinetic models, including the species that deactivate the catalyst [82, 194, 195] . This is the step that turns analysis into a design tool for catalysts and processes.

Goals. Our goals are grouped in these lines of work:

  • Analytical workflow: integrate analytical techniques into one quantitative protocol per feed type, with mass balance closure across gas, liquid and solid phases.
  • Wasteometrics I: quantitative, molecular-level analysis of feeds, products and trapped species, including heteroatoms and polyaromatics.
  • Wasteometrics II: data mining and processing; filter, extract, curate, represent and visualize large analytical datasets, define lumps and molecular descriptors, and apply machine learning.
  • Wasteomics: convert molecular data into reaction networks, deactivation functions and kinetic models, using population balances, descriptor-based and surrogate models.

Applications. We apply this competency to:

  • Thermo-catalytic valorization of plastic waste, tires and municipal solid waste.
  • Hydrothermal liquefaction and hydrotreatment of algae, with reuse of the aqueous by-products.
  • Quantitative characterization of raw and upgraded bio-oils from a commercial biorefinery [186] .
  • Thermal fouling of hydrocarbons at high temperature and pressure, and crude oil fouling in heat exchangers.
  • Coke formation and deactivation in oligomerization and oxygenates-to-hydrocarbons processes [109, 138, 196] .

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Related Publications

Coking and Sintering Progress of a Ni Supported Catalyst in the Steam Reforming of Biomass Pyrolysis Volatiles

by Ochoa, Arregi, Amutio, Gayubo, Olazar, Bilbao, Castaño
Appl. Catal. B: Environ. Year: 2018

Abstract

The valorization of biomass (pine wood) for hydrogen production has been studied in a two-step process, comprising pyrolysis and subsequent steam reforming of the volatiles produced in the first step. This work focuses on the deactivation of the Ni commercial catalyst used in the second step. Pyrolysis of biomass has been performed in a conical spouted bed reactor at 500 °C, and the in-line catalytic steam reforming of the pyrolysis volatiles, in a fluidized bed reactor at 600 °C. Deactivated catalyst samples were recovered at different values of time on stream, and analyzed by means of XRD, N2 adsorption-desorption, SEM and TEM microscopies, TPO, Raman and FTIR spectroscopies. The results show that the deactivation is mainly due to the encapsulation of Ni particles by coke, together with Ni sintering, to a lesser extent (from a Ni particle size of 25 nm in the reduced fresh catalyst, to 39 nm at 100 min). The former is ascribed to the condensation of oxygenates (particularly phenols), and the latter is inevitable within the current conditions. As the fraction of uncovered Ni particles decreases with time on stream, the deposition of encapsulating coke is slowed down (from a formation rate of 0.30 mgcoke gcatalyst−1 min−1 to 0.20 mgcoke gcatalyst−1 min−1, at 0–50 min and 50–100 min on stream, respectively), promoting the deposition of coke on the catalyst support (with a formation rate of 1.04 mgcoke gcatalyst−1 min−1 at 50–100 min on stream), with a more carbonized structure and formed through the thermal decomposition of phenols in the reaction medium.

Keywords

REF W2C ANW CRE