Solvent waste is one of the most significant environmental and cost challenges in chemical manufacturing, coatings, adhesives, and pharmaceutical production. It contributes directly to Scope 1 emissions, generates hazardous waste that requires expensive disposal, and represents raw material that has been purchased and then discarded.

The conventional approach to solvent waste reduction is engineering-led: install recovery equipment, switch to lower-VOC formulations, or redesign the process. These are valid interventions, but they require capital investment and often significant process development work.

There is another approach that is frequently overlooked: optimising the existing process to reduce solvent use and waste before considering capital investment. In most manufacturing operations, the process is not running at its optimal point. Solvent use is higher than it needs to be, not because the process is fundamentally inefficient, but because the operating parameters have never been systematically optimised.

Machine learning can find those operating parameters — and the data needed to do it is almost always already being collected.

Where solvent waste comes from

Before optimising, it helps to understand the sources of solvent waste in your process. The main categories are:

Excess solvent in the formulation. Many formulations use more solvent than is strictly necessary for the application properties required. The solvent level was set during development and has never been revisited. A systematic analysis of the relationship between solvent content and application properties often reveals that the specification can be tightened without affecting product performance.

Process losses. Solvent that evaporates during mixing, coating, or drying — beyond what is required for the process — represents waste. The amount of process loss depends on temperature, airflow, equipment design, and process timing. Optimising these parameters can reduce losses significantly.

Cleaning solvent. Solvent used for equipment cleaning is often the largest single source of waste in batch manufacturing operations. The amount of cleaning solvent required depends on the previous product, the equipment geometry, and the cleaning procedure. Machine learning can identify the combinations of previous product and equipment state that require the most cleaning, and optimise the cleaning procedure accordingly.

Off-specification product. Product that fails quality testing and must be reworked or scrapped takes its solvent content with it. Reducing batch failure rates reduces solvent waste proportionally.

How machine learning approaches the problem

The starting point is your existing process data: batch records, solvent usage logs, quality results, and any environmental monitoring data you collect. This data contains the information needed to identify the process parameters that drive solvent use and waste.

The analysis proceeds in two stages.

Stage 1: Identify the drivers. A machine learning model is trained on the historical data to identify which process parameters are most strongly associated with solvent use and waste. This is not a simple correlation analysis — it captures non-linear relationships and interactions that conventional analysis would miss.

The output of this stage is a ranked list of the process parameters that matter most, with an estimate of the potential reduction achievable by optimising each one.

Stage 2: Optimise the operating point. Once the key drivers are identified, the model is used to identify the combination of parameter settings that minimises solvent use while maintaining product quality. This is a constrained optimisation problem: minimise solvent use subject to the constraint that quality specifications are met.

The result is a set of recommended operating parameters — specific, actionable, and grounded in your actual process data.

What the data typically shows

In our experience working with manufacturing clients, the analysis typically reveals two or three process parameters that account for the majority of the variation in solvent use. These are usually parameters that are already being measured and recorded, but have never been analysed in relation to solvent consumption.

Common findings include:

• Temperature profiles that are set conservatively to avoid quality problems, but which could be tightened to reduce evaporative losses without affecting product performance
• Mixing times that are longer than necessary, increasing exposure time and evaporative losses
• Raw material moisture content that drives variability in solvent demand, suggesting a tighter incoming material specification
• Cleaning procedures that use more solvent than necessary for certain product transitions, suggesting a revised cleaning matrix

Each of these findings translates directly into a process change that can be implemented without capital investment.

The carbon footprint connection

Solvent waste reduction is not just a cost and compliance issue. It is a direct contributor to Scope 1 emissions reduction.

Volatile organic compounds (VOCs) released during manufacturing are a direct greenhouse gas emission. Solvent that is recovered and recycled rather than emitted represents a reduction in Scope 1 emissions. Solvent that is not used in the first place — because the process has been optimised — represents an even larger reduction.

For manufacturers with net zero commitments, process optimisation is one of the most cost-effective routes to Scope 1 reduction. The investment is in analysis and process adjustment, not in capital equipment. The returns are immediate and measurable.

Getting started

The first step is to understand your current solvent use and waste profile. How much solvent do you purchase? How much ends up in the product? How much is lost to evaporation, cleaning, and off-specification product?

If you have this data — even approximately — you have the foundation for an optimisation analysis. The next step is to structure your process data and identify the parameters that are most likely to be driving the variation.

We can help with both steps. The analysis typically takes four to six weeks, and the findings are usually sufficient to justify the investment many times over.