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Process Intensification: A Prerequisite for Success in Custom Manufacturing Chemspec 2016, Basel Dr. Christoph Schaffrath Dr. Guido Giffels Slide 1 June 1st, 2016 Saltigo Who are we and where do we come from? A globally operating company

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SLIDE 1

Slide 1 June 1st, 2016

Process Intensification: A Prerequisite for Success in Custom Manufacturing

Chemspec 2016, Basel

  • Dr. Christoph Schaffrath
  • Dr. Guido Giffels
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SLIDE 2

Chemspec Basel 2016, Saltigo Presentation Slide 2 June 1st, 2016

Saltigo profile

  • customers:

ca. 150

  • employees:
  • ca. 1.250
  • products/projects: ca.

400

  • 10 production plants

Leverkusen + Dormagen (Ger) Lanxess profile

  • spin off from Bayer 2004
  • employees: 16,225
  • sales: € 7.9 billion in 2015
  • global footprint: 29 countries
  • 52 production sites

A globally operating company for exclusive synthesis and innovative fine chemicals Core competence

Market-orientated, custom manufacturing service provider Outsourcing partner for fine chemicals 2005-2006 Since 2006 Saltigo – a company

  • f the LANXESS

group Business Unit Fine Chemicals

  • f LANXESS

Saltigo

Who are we and where do we come from?

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SLIDE 3

Chemspec Basel 2016, Saltigo Presentation Slide 3 June 1st, 2016

  • Exclusive production 100 - 5.000 kg/a
  • Intermediates and APIs
  • Regulated area (CGMP, etc.)
  • Production volumes > 1.000 t/a
  • Intermediates and AIs
  • Regulated area (Biocides Regulation, etc.)
  • Often non-exclusive products
  • ISO-production or special demands

Fine chemicals Agro chemicals Pharmaceuticals

Custom Manufacturing

Saltigo – A global player in custom manufacturing serving different industries

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SLIDE 4

Chemspec Basel 2016, Saltigo Presentation Slide 4 June 1st, 2016

Focus on market oriented services:

Support of customer needs along the complete project lifecycle

Raw Material Advanced Intermediate Active Substance Formulation Customer

Core competence of Saltigo Process Intensification: A prerequisite for successful custom manufacturing

  • Custom manufacturing/synthesis up to 5,000 t/a
  • Enhanced service support (registration, analytics, etc.)
  • Professional procurement, reliable supply chain

Idea Market Process development Pilotation Lab Production

  • Custom-made process development
  • Efficient project management
  • Continuous improvement process
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SLIDE 5

Chemspec Basel 2016, Saltigo Presentation Slide 5 June 1st, 2016

Data Generation Innovation Continuous Process Improvement Teams Mindset Customer

Process Intensification

Process Development Analytics

QA and QC

Technology Plants

Debottlenecking Capacity

Project Management Milestones and Implementation

Economy Investments

Process intensifications drive efficiency and cost optimization

A diverse toolbox is required for a successful implementation

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SLIDE 6

Chemspec Basel 2016, Saltigo Presentation Slide 6 June 1st, 2016

Process intensifications –

What does it mean?

„Getting More out of Less“

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SLIDE 7

Chemspec Basel 2016, Saltigo Presentation Slide 7 June 1st, 2016

Ways to achieve this goal

Getting More out of Less –

How?

  • 1. More „Right 1st Time“, optimizing process & parameters to improve

product quality, reducing/omitting number of process steps, reworks, …

  • 2. Changing Equipment (Hardware) 

improve setup

  • 3. Higher Space-Time-Yields by Process Optimization,

e.g. shortening cycle time, increasing output/batch etc.

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SLIDE 8

Titel /as Folie 8 19.05.2016

Case #3 Case #2 Case #1 Shortening cycle time of an exothermic reaction by use of an intelligent control factor Significant increase of production capacity & productivity by stepwise modification of reactor setup Increasing the bulk density of an agrochemical product by using multivariate data analysis

Process Intensification

3 Case studies

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SLIDE 9

Chemspec Basel 2016, Saltigo Presentation Slide 9 June 1st, 2016

  • Mod. B
  • Mod. A

Case Study #1

Objective: increase the bulk density of a crystallized product

  • Thermodynamically more stable,

may be formed out of Mod. A

  • Required product form by the customer
  • Gives lower purity if crystallized directly
  • Mechanically more stable, larger particle size 
  • Gives higher bulk density
  • Crystallizes kinetically controlled („faster“)
  • Gives better & required purity
  • Mechanically less stable 
  • Grinding during drying leads to low particle size and

to low bulk density

  • Bulk density is crucial, as a certain amount of

product per big bag is asked by the customer

Case description

  • Large-scale custom-made product crystallizes in (at least) 2 polymorphorphic modifications
  • Customer requires Mod. B, a defined purity (specification!) and a high bulk density
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SLIDE 10

Chemspec Basel 2016, Saltigo Presentation Slide 10 June 1st, 2016

Objective: How to get…

Case Study #1

Objective: increase the bulk density of a crystallized product

  • the right modification (Mod. B) ?
  • the right purity ?
  • a good bulk density ?

… with a mimimum of effort? Crystallize Mod. B directly: Purity not sufficient

„Is this the best option?“ Let‘s look deeper into this one

V

Crystallize A first, isolate & recrystallize, seeding B: Works, but additional (re)crystallisation (= effort)

?

Convert Mod. A  Mod. B on the dryer Possible, but drying process gave varying results

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SLIDE 11

Chemspec Basel 2016, Saltigo Presentation Slide 11 June 1st, 2016

Case Study #1: Multivariate Data Analysis –

Tool to pick the right parameters and values

Data Parts 1-2 v03.M11 (OPLS)(BLM) Sources of Variation Colored according to Var ID ($SourceID)

Einsatz aus F2929 Inertisieren Evakuieren Kühlen Belüften Trocknen Ende Austragen p[1]

  • 0,03
  • 0,02
  • 0,01

0,01 0,02 36 73 109 146 189 294 10 10 42 74 106 138 170 205 251 306 367 665 34 62 9 47 86 175 Var ID ($MaturityID) R2X[1] = 0,111 E10 P80A P80B P81 R15Y R60Y R85Y S10 T10 T60 T61 T80 T81

SIMCA 14.1 - 2016-02-18 15:10:14 (UTC+1)

Thermal impact triggers the modification change from Mod. A  Mod. B Drying Process (not automated) gave various results in bulk density Multivariate Data Analysis „highlighted“ the crucial process parameters during drying:  Pressure (vacuum) – higher pressure in the beginning is better  Bulk temperature – higher temperature in the beginning is better  Energy application (by stirrer): lower is better – grinding!

Multivariate Data Analysis – normalized data showing effect on bulk density Drying Process

Pressure Temperature Energy input

Parameter data from approx. 60 batches  high amplitude = large impact on bulk density

Drying Charging Cool, areate, discharge

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Chemspec Basel 2016, Saltigo Presentation Slide 12 June 1st, 2016

Case Study #1: Increase Product Bulk Density

Rationale behind the “right” parameters

A-wet  A-dry  B-dry: grinding of Mod A during drying  low bulk density A-wet  B-wet  B-dry: formation of stable Mod B first, then drying,  larger particles and higher bulk density

Rationale

V

Possible „Routes“

 First tempering the wet product at higher pressure („bad“ vacuum) and resulting higher inner temperature leads to fast change from Mod. A  Mod. B = less grinding of mechanically less stable Mod. A, resulting in a better bulk density after drying.

?

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Chemspec Basel 2016, Saltigo Presentation Slide 13 June 1st, 2016

Bulk Density before/after Optimization

Case Study #1: Increase Product Bulk Density

Results after optimization

  • Relevant process parameters were identified

by means of multivariate data analysis

  • Significant increase of bulk density was

achieved

  • Requested amount of product per big bag can

be filled Summary

5 10 15 20 25 , 2 7 , 2 9 , 3 1 , 3 3 , 3 5 , 3 7 , 3 9 , 4 1 , 4 3 , 4 5 , 4 7 , 4 9 , 5 1 , 5 3 , 5 5 , 5 7 , 5 9 , 6 1 , 6 3 , 6 5 , 6 7 , 6 9 , 7 1 , 7 3 , 7 5 , 7 7 , 7 9 Part 1 Part 2

Histogram of Bulk density

Before Optimization Optimized

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Chemspec Basel 2016, Saltigo Presentation Slide 14 June 1st, 2016

Case Study #2: Increasing Capacity & Productivity by Optimized Reactor Setup

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Chemspec Basel 2016, Saltigo Presentation Slide 15 June 1st, 2016

Case Study #2

Increasing capacity & productivity by optimized reactor setup

Case description

  • Large scale chlorination product (B) was produced in batch mode with limited capacity
  • (A) reacts to target product (B). (B) reacts further to byproduct (C) in a consecutive reac-tion. To

maximize selectivity, (A) is only partially converted and recycled during workup

  • Objective: increase capacity / productivity to meet market demand

Bottleneck

A B

Cl2

C

Cl2

Target Product

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Chemspec Basel 2016, Saltigo Presentation Slide 16 June 1st, 2016

Szenario 2): First expansion

  • Add 2nd distillation unit
  • Change to continuous operation of distillation:

Unit 1: recycle (A) Unit 2: isolate product (B)

  • Doubling chlorination unit

Bottleneck

(A) (B) (C)

A B

Cl2

C

Cl2

continuous

Case Study #2

Increasing capacity & productivity by optimized reactor setup

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SLIDE 17

Chemspec Basel 2016, Saltigo Presentation Slide 17 June 1st, 2016

Szenario 3): Debottlenecking distillation

  • Reactor setup as szenario 2, plus …
  • Improved column for distillation unit 1

continuous

A B

Cl2

C

Cl2

(C) (B) (A)

Bottleneck continuous

Case Study #2

Increasing capacity & productivity by optimized reactor setup

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SLIDE 18

Chemspec Basel 2016, Saltigo Presentation Slide 18 June 1st, 2016

Szenario 4): Optimized Use of Chlorination Unit

A B

Cl2

C

Cl2

(A) (C) (B)

  • Chlorination changed from 2 batch reactors to only one CSTR

(continuously stirred tank reactor) with higher throughput

  • CSTR = broader residence time distribution  higher conversion needed for

the same capacity  decreased selectivity, higher portion of (C)

continuous continuous (CSTR)

Case Study #2

Increasing capacity & productivity by optimized reactor setup

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SLIDE 19

Chemspec Basel 2016, Saltigo Presentation Slide 19 June 1st, 2016

Summary

  • Overall, 4 fold increase of production capacity at doubled productivity (!)
  • Further upsides:

> back integration into raw material via Lanxess production network > further conversion of byproduct (C) into sales product improves overall efficiency

Case Study #2

Increasing capacity & productivity by optimized reactor setup

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Chemspec Basel 2016, Saltigo Presentation Slide 20 June 1st, 2016

Case Study #3: Shortening cycle time by intelligent use

  • f an appropriate control factor
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SLIDE 21

Chemspec Basel 2016, Saltigo Presentation Slide 21 June 1st, 2016

Case Study #3

Shortening cycle time by intelligent use of an appropriate control factor

Case description

  • Grignard reaction of an aryl chloride (Ar-Cl  Ar-Mg-Cl)
  • sluggish, but highly exothermic
  • accumulation of reaction potential must be avoided to prevent runaway reaction
  • Standard procedure

 Magnesium + solvent are charged to the reactor  Portion of aryl chloride is added  Await start of reaction (exotherm = heat release; sampling)  Further aryl chloride is added continuously over time (fixed rate), controlling/entsuring that exothermic reaction continues

  • Objective:

How to control the reaction progress intelligently to allow maximum speed of addition – ?

Time

Mass of Ar-Cl added Heat release

Main Reaction Rxn start

Adapted from Kryk et. al., see: Organic Process Research & Development, 2007, 11, 1135-1140

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Chemspec Basel 2016, Saltigo Presentation Slide 22 June 1st, 2016

Case Study #3

Shortening cycle time by intelligent use of an appropriate control factor

Approach for SAFE but FASTER addition rate of reagent Ar-Cl

  • Key: Accumulation of Ar-Cl must be avoided
  • Reaction is run in a closed reactor, allowing reaction temperature above boiling point
  • f the solvent  faster reaction initiation and conversion of Ar-Cl  Ar-Mg-Cl
  • Recording of the reactor‘s calorimetric data installed, allowing a real-time heat balance
  • From calorimetric data, the theoretical maximum reactor pressure in case of hypothetical

immediate spontaneous full conversion (adiabatic increase of p and T) is calculated, the so called pMTSR (pressure at Maximum Temperature of the Synthesis Reaction)

  • The pMTSR is a value for the accumulated reaction potential!

Always staying below the maximum allowed pMTSR as lead parameter, the maximum addition rate of the aryl halide can be applied  shorter cycle time

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Chemspec Basel 2016, Saltigo Presentation Slide 23 June 1st, 2016

Standard Procedure Optimized Addition based on pMTSR

Case Study #3

Shortening cycle time by intelligent use of an appropriate control factor

Time Pressure, Dosage Rate

Dosage Rate Ar-Cl Reactor Pressure pMTSR pMTSR Limit

Using the appropriate control factor allows significant cycle time reduction

Time

Dosage Rate Ar-Cl pMTSR pMTSR Limit pMTSR Target

Improved Dos. Time Initial Dosage Time

Pressure, Dosage Rate

Graphics adapted from H. Kryk et. al., HZDR

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SLIDE 24

Chemspec Basel 2016, Saltigo Presentation Slide 24 June 1st, 2016

Summary

Take-Home Messages

Successful process intensification requires:  Knowledge Data, Know-How, Competence, Experience, Ideas  People  Ressources Equipment, Technology, Budget, Hands and Heads  People  Mindset Objectives, Planning, Interdisciplinary Team-Work  People  Acknowledgement to the Saltigo Project Teams!

Thank you for your attention!

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SLIDE 25