Economic Optimization of Integrated Nutrient and Energy Recovery - PowerPoint PPT Presentation

Economic Optimization of Integrated Nutrient and Energy Recovery Treatment Trains Using a New Model Library Céline Vaneeckhaute, Université Laval Evangelina Belia, Primodal Inc. 7 th International Conference on Sustainable Solid Waste Management, Heraklion, Greece, June 26-29, 2019

Outline of the presentation Introduction Nutrient recovery model (NRM) library T reatment train optimization T ake-home message 2 2

INTRODUCTION 3

The nutrient paradox Nutrient excesses Increasing demand for  Environment chemical fertilizers Environmental pollution Nutrient depletion (P, K) 4

How to produce more food and energy with less pollution? 5

Nutrient recovery from (digested) waste: A potential sustainable and cost-efgective solution • Precipitation → struvite, calcium phosphates • Ammonia stripping → NH 3 • Acidic air scrubbing → ammonium sulphates • Membrane fjltration → H 2 O, N-K concentrates • Biomass production and harvest → biomass • … ⇒ Mainly physicochemical unit processes ! ⇒ Mainly physicochemical unit processes !

Potential fmow diagram of a biorefjnery for nutrient and energy recovery Problem: Optimal combination difgerent for each waste stream Problem: Optimal combination difgerent for each waste stream Research question: What is the optimal combination of unit Research question: What is the optimal combination of unit processes and what are the optimal operating conditions? processes and what are the optimal operating conditions?  Given: Particular waste stream  Given: Particular waste stream   Optimal: Optimal: • Maximal resource recovery (nutrients, energy) • Maximal resource recovery (nutrients, energy) • Minimal energy and chemical requirements • Minimal energy and chemical requirements Approach = Mathematical models Approach = Mathematical models

Nutrient recovery model (NRM) library 8

Generic nutrient recovery model (NRM) library Modelica language TORNADO/WEST NRM-Prec NRM-Scrub NRM-AD NRM-Strip NRM = Nutrient Recovery Model

Combined three-phase physicochemical-biological models Reactor model Fast reactions Chemical PHREEQC PHREEQC speciation model Challenge = Challenge = Interface numerical solution! numerical solution! Species Species pH pH Slow TORNADO/WEST TORNADO/WEST reactions Physico- Biochemical chemical model model

TREATMENT TRAIN OPTIMIZATION 11

Global sensitivity analysis (GSA) • Selection of factors with the highest impact on model outputs (= objective for further study) • Input waste stream characteristics • Kinetic model parameters • Process operational parameter • …. Acquired understanding Optimal treatment train confjguration Optimal treatment train confjguration

Treatment train confjguration Target = struvite + ammonium sulfate OPTIMAL OPERATING CONDITIONS? OPTIMAL OPERATING CONDITIONS? C-recovery P-recovery N-recovery Use of Chloride inhibition  Use of Chloride inhibition  Mg(OH) 2 /MgO Phosphate inhibition  Mg(OH) 2 /MgO Phosphate inhibition  Ca-inhibition  Removal of Ca, Fe Ca-inhibition  Removal of Ca, Fe Scaling  Fe/Al impurities  and Al precipitates Fe/Al impurities  and Al precipitates Consumables →Costs Recovered products → Revenues

M M I A X N I I M M I I Z Z E E Treatment train optimization 14

Treatment train optimization: Economic analysis Variable costs & revenues Capital costs Variable costs & revenues Capital costs • Heat requirements  worst & best case • T echnology providers • Chemicals • CAPDET software • Electricity • Maintenance, material & labor costs • Biogas production  electricity and heat • Fertilizer marketing  worst and best case • CO 2 emission reduction credits: 15 $ ton -1 15

Treatment train optimization: Economic analysis Financial benefits: ~ variable costs: 5 $ m -3 manure y -1 Optimized 90 $ ton -1 solids y -1 Biorefinery ~ variable + capital costs: 2 $ m -3 manure y -1 40 $ ton -1 solids y -1 Subsidies ZeroCost-Biorefinery (pay-back time: 7 years) Heat balances 16

Take-home message 17

Main conclusions • Generic nutrient recovery model (NRM) library created and validated • Global sensitivity analysis  Identifjcation of interaction between processes  Optimal treatment train confjguration • Model-based treatment train optimization  Valuable tool for evaluation of project feasibility  Key factors for design of nutrient and energy recovery facilities: • subsidies • fertilizer marketing potential • heat balances 18

Perspectives: Development of a decision-support tool for optimization of holistic organic waste valorization chains Industry/ Research Government Consultants 19

Further reading 20

Questions ? $$$ Modelling is a must for optimizing the value chain! « Nothing is lost, Nothing is created, Everything is transformed » celine.vaneeckhaute@gch.ulaval.ca https://bioengineblog.wordpress.com/ 21