Chemical Process Manufacturing Characteristics

There are basically three types of chemical products; commodity chemicals, industrial- intermediate chemicals and consumer products. The chemical manufacturing processing strategy for each type of chemical product has its own unique competitive and comparative advantages. The commodities are normally manufactured in high capacity using continuous process, Industrial products in medium capacity using continuous plants and consumer products mostly in small capacity plants with unique value addition opportunities depending on the products and manufacturing locations. Generally, commodity chemicals become feed stocks for industrial products and industrial- intermediate products are feed-stocks for consumer products with some variations depending on the type of products.

Natural Resources: Crude oil, Gas, Natural minerals, Natural fibers, agricultural feed stocks etc.

Basic commodity chemicals: Basic commodity chemicals such as ammonia, ethanol, methanol, ethylene, propylene, vinyl chloride, specialty chemicals, ethylene glycol, ethylene oxide, fuels, solvents etc. are manufactured in large quantities.

Industrial-intermediate Products: Fibers, resins, films, PVC / PP resins, paper, polymeric materials, urea etc.

Generally manufacturing processes of commodity chemicals and industrial chemicals are based on matured and patented technologies for which process licensing is required. However, many consumer chemicals manufactured in small quantities which are generally called specialty chemicals involving chemical reaction processes under mild operating conditions. The unit processes and operations are specific to the reactants and end-products where there is a huge demand for technology development expertise.

In line with the customer demands, it is imperative to continually identify or design new chemical product meeting the required performance and subsequently develop the innovative process technologies to manufacture it.

While studies on market-oriented product development have identified several general dimensions of market knowledge used in product innovation, the nature of market knowledge that is specifically important in the chemical industry remains unclear. Because firm relevant knowledge resources are increasingly seen as being industry specific, filling this gap becomes more relevant. This study uses a multiple case study of six product innovation projects in six different companies to identify important market knowledge dimensions in the product development of chemical firms. Aggregated results from the six cases point to segment knowledge, application knowledge, product usage knowledge, and customer knowledge as being important market knowledge dimensions. Implications for theory and practice as well as avenues for further research are included.

Chemical Product Design based on the required product properties and specifications with the help of commercially available Computer Aided Molecular Design (CAMD) / Quantitative Structure Activity Relationship (QSAR) software with Chemical Product Property Model with Knowledge Database. When a new chemical is envisioned, the design project can be challenging in terms of meeting the product specifications, safety / environmental regulations, mode of manufacturing, market demand and cost competitiveness, however, the following road map is expected to reduce cognitive load. Identify, where unmet needs or problem for want of a product or process

Acquiring specific molecular knowledge of variety of molecules / compounds, find out the required product properties including but limited to physical or chemical or thermal or esthetic through property Database Develop property model using CAMD software (virtual lab), validate the results through study electronic / atomic / molecular structures of variety of atoms, compounds / chemicals and their associated properties Verify the model generated product properties in the laboratory and ensure the properties generated in the virtual laboratory are in line with that of physical laboratory

Build virtual chemical laboratory: Acquire licensed reactions database such as Thieme of Science of Synthesis / SPRESI / ChemInform of Wiley, Acquire chemical synthesis literature database, maintain precursors catalogs with suppliers data, Computer Aided Synthesis Design (CASD) software (ICsynth) with the requested product as input, will generate chemically meaningful precursors from accredited reaction databases with alternative paths through retro-synthetic analysis, Validate ICsynth suggested process route using the reaction literature or analogous reaction chemistry / synthesis and decide if the proposed synthesis path is acceptable in terms of reaction conditions, feed stocks and availability precursors points of view Verify the process scheme through process simulation / in physical laboratory / pilot plant

Develop demonstration / Commercial Plant Process Design using commercially available software such as CHEMCAD: Perform detailed process simulation, Perform detailed reactions networks design, Perform detailed separations trains design, Perform detailed heat - power integration, Perform detailed equipment design, Perform optimization, Prepare piping & instrumentation diagrams, Develop detailed equipment specifications, Develop plot plans and equipment layouts, Perform Front Engineering Design (FEED), Estimate project Capex / Opex cost and Reconfirm economic / investment evaluation.

Typical Steps from Chemistry to Commercialization

1. New chemical product

Identification of new products to meet the market needs in terms of new demand / solving the existing user (business) problems related to either economics or ecology For given a set of target properties - determine the molecule or molecular structure that matches these properties using Computer Aided Molecular Design (CAMD) method to generate chemically feasible molecular structures, estimate the target properties for the generated structures, screen/select those that satisfy the specified property constraints.

CAMD methods based on macroscopic properties where the molecular structure is represented by groups and/or connectivity indices are suitable for design of relatively smaller molecules either as chemical products or as fuel / polymer additives (or ingredients) / solvents / refrigerants for formulated products. There is a need to develop knowledge-based systems that may guide the chemical product designer to not only identify the target properties but also to specify their target (goal) values for a large range of chemical product design problems. The selection of target properties should also be closely linked with what can be estimated (and therefore, computed) and what must be measured? The knowledge-based system can help to reduce the number of experiments or to focus on a few specialized measurements from which a number of other target properties may be estimated. For example, if the solvent molecule type for a complex (large multifunctional molecule) solute can be identified, then experiments to measure solubility can be concentrated on some representatives of the identified molecular type to generate not only the unavailable property model parameters but also to identify the desired solvent. For a complex molecular structure of the solute, it is unlikely that the needed property model parameters would be available at the start of the problem solution. If the chemical product design involves molecules of larger size, distinction among isomers and or different molecular structures for the same chemical compound type becomes more important. Consequently, the molecular structural representation becomes more complex using smaller and smaller scales while the property prediction becomes more specialized.

Multiple property models at different scales or levels of molecular structural variables will need to be considered if the isomers and/or multiple conformations are also going to be considered, a communication (link) between lower-level modeling tools and higher leveling model tools also need to be established. The idea is to first establish the molecular type in the search/design through macroscopic properties and then to link the promising candidates to higher-level mesoscopic or microscopic methods for more detailed analysis. Start with a molecular description at the group level, which is then converted to a 2-dimensional atomic representation at the atomic level. This is then passed to molecular modeling software that converts the atomic representation to a 3-dimensional model, which is then optimized to obtain the final 3-dimensional structure. Once the optimized structure has been obtained, a whole range of descriptors and properties may be estimated.

Target properties usually include pure component as well as mixture properties and the selection of the property estimation model(s) raises other issues & needs, for example, uncertainties in property estimations, availability of model parameters and size of the search space. Another difficulty is associated with unavailable model parameters. If model parameters are not available for a generated molecule and a corresponding property, that generated molecule can no longer be considered as one of the candidates since its properties cannot be estimated. This may eliminate a potentially optimal molecule. The need therefore is to develop property estimation models with fewer parameters but having larger application ranges. In principle, property models suitable for CAMD methods need to be predictive. Therefore, further development of CAMD methods for applications in structured products and formulations is closely related to the availability and usability of the needed property models.

For design of complex molecules where a higher level of molecular structural information needs to be considered in order to search among isomers, the CAMD methods usually employ problem specific models based on property-molecular structural relationships. Because the molecular structure plays an important role in the estimation of properties related to the design of these large molecules, Quantitative Structure Activity Relationship (QSAR) based methods have become quite popular for these types of design problems. Properties estimated through parameters obtained from dynamic modeling and/or molecular modeling is necessary when microscopic and/or mesoscopic scales have been employed for molecular structural representations. The need is to develop special quantitative property models based on the data generated from dynamic and/or molecular modeling plus any available experimental data. The property estimation task could be arranged on a hierarchy based on the computational effort and cost related to obtaining a property value. Obviously, the experimental measurement of the property should be at one (high) end and simple, first-order group contribution methods could be at the other (low) end. The largest number of compounds of different types is handled at the lower end and as one proceeds upwards, the number of compounds of different types decrease but the number of isomers that can be handled increase. In this way, the computationally intensive calculations are saved only for those candidates that have satisfied all other constraints based on the lower-level property models. Note that even in this approach, the uncertainties of prediction accuracy may eliminate some candidates. On the other hand, the method would systematically move towards the solution, provide useful insights and keep the computational load at a manageable level. If pure component and mixture properties were needed in a CAMD problem, the pure component properties would be estimated first. This would reduce the computational load significantly for the estimation of mixture properties. Also, this may make the mixture property model more acceptable since some molecules that could not otherwise be handled would be removed due to a specified property constraint and not because of unavailable model parameters.

Typical pure component (macroscopic) properties are boiling points, melting points, heat of vaporization, partition coefficients, viscosity, surface tension, thermal conductivity, solubility parameter and many more. Typical properties from molecular modeling or quantum mechanics are bond energies, interaction energies, binding energies, etc. When working with large complex molecules, the structural changes in the molecular structure (for example, in isomers) need to be observed in a defined activity or property. Therefore, special QSAR based models are developed and used in design of special purpose molecules.

In the area of mixture properties, solubilities of solids, liquids and gases in solvents is a very common target property, mixture viscosities and diffusivity are also quite common for CAMD problems dealing with solvents. Properties related to different combinations of phase equilibrium involving vapor, liquid and solid are quite common. If the solute molecules are not large and complex, macroscopic properties from group-contribution methods are usually sufficient, provided the necessary group parameters are available. For large, complex molecules and or higher-level property modeling, special models based on quantitative structure activity relationships may need to be developed.

2. New Process Scheme

Identification of new processing scheme to the existing product solving the existing problems related to either economics or ecology through a retro-synthetic analysis from relatively complex target molecule to various simple precursors levels up to the economically available starting raw materials using Computer Aided Synthesis Design (CASD). Validate CASD generated process scheme through process simulation followed by laboratory test and then pilot plant as deemed fit.

Perform process simulation with the most relevant reactor / thermodynamic / property / equipment performance models to meet the following objectives:

• To integrate the entire processing scheme with the required unit process and operations

• To ascertain process & thermodynamic feasibility

• To arrive at exploratory process scheme

• To develop inductive plant design with the capex and Opex inputs

• To capture directional economics

1. Conduct Lab scale experiments and establish the proof of concept and meet the following objectives.

• To ascertain conversion chemistry & yield

• To arrive at operating pressure & temperature

• To check catalyst selectivity & efficacy

• To capture reaction kinetics data

• To understand reaction mass properties

The laboratory experimentation provides at the early stage of the project clear understanding of the kinetics / conversions / expected performance with a little cost thus reducing reaction / conversion risks. The chemical laboratory services would be obtained through well-established NCL Pune, IIP Dehradun etc. with specific roles and responsibilities including but not limited to Catalyst screening, reactions in laboratory scale with corrosive compounds, high pressure reactions in batch and continuous mode, Separation (filtration, distillation, drying) and Development of analytical methods.

2. Design, fabricate and build a Pilot Plant Skid to meet the following objectives:

• To validate lab scale data: conversions / kinetics

• To confirm cost effective material of construction

• To ascertain process controls & safety interlocks

• To estimate consumption of utilities for reactions

• To ensure reactor operability & safety

3. Scale-up and design the demonstration / commercial plant to meet the following objectives:

• To ascertain plant operability, reliability & durability

• To confirm process performance & utilities figures

• To validate entire plant controls & safety interlocks

• To capture plant maintainability

• To validate economics of process technology

4. Estimate project cost and perform techno-commercial viability analysis

5. Develop Intellectual Property strategy

6. Build commercial demonstration plant

3. Protection of Intellectual Property

If the technology is patentable, then it can be protected (if such protection is available where the enterprise is located). There are both product and process patents, and it is important to understand what is “key uniqueness, innovativeness & utility” about the product / process and the value proposition in order to know how to protect the product. In other circumstances, patenting might not be suitable. There are primarily three ways to protect IP in these industries legal patents, time scale economy and trade secrets. Trade secrets are challenging to protect, because once the product is sold it can be reverse engineered and copied. Patenting plan is envisaged in the second year.

4. Risks and Rewards

• Product & process development journey is a tough terrain with many pits and pot holes

• It is however, a thrilling & exciting journey for a competent & committed teams

• Innovative product & process technology development competencies are essential ingredients for any company to survive and also prosper under the present & future competitive, volatile and uncertain business environment.

• The product & process technology strategy shall be designed prudently with minimal risks & maximum rewards.