Palm Oil Waste Has Hidden Value: How AI Could Help Mills Recover More Resources

Researchers place palm oil mill effluent, commonly called POME, at the centre of a framework linking water, energy, nutrients and solid residues. Their proposal combines artificial intelligence with resource accounting to help operators understand how one decision affects the wider system.

Palm oil production leaves behind wastewater, fibres, shells and other residues that still contain useful energy and materials. Mill operators face decisions about treating polluted water, supplying steam, recovering nutrients and finding worthwhile uses for leftover biomass. Managing these tasks separately can hide opportunities to reduce waste, along with costs that appear elsewhere in the mill.

A review published in Resources, titled "Artificial Intelligence for the POME-Centred Water–Energy–Waste Nexus in Palm Oil Production: Towards a Resource-Nexus Digital Twin," examines these connections. Kian Lun Soon and colleagues place palm oil mill effluent, commonly called POME, at the centre of a framework linking water, energy, nutrients and solid residues. Their proposal combines artificial intelligence with resource accounting to help operators understand how one decision affects the wider system.

The researchers reviewed 109 sources through a structured screening process, drawing mainly on English-language journal articles published from January 2020 to May 2026. Their evidence included 78 records from palm oil systems, 23 from comparable systems and eight reviews, with proposed integrations labelled separately from demonstrated capabilities. The resulting digital twin is a research proposal whose performance has not been tested in an operating palm oil mill.

Why Palm Oil Wastewater Deserves a Closer Look

The review cites estimates of about 3.05 tonnes of POME generated for every tonne of crude palm oil, alongside water use of 5.0–7.5 tonnes. This acidic wastewater contains organic matter, suspended solids, oil, nutrients and trace metals that can damage receiving waters when poorly managed. Its composition changes between mills and over time, making treatment settings that work at one location unreliable elsewhere.

POME connects several resource streams because it receives wastewater from multiple processing stages and can supply recovered water, energy and nutrients. Anaerobic digestion uses microorganisms to break down organic matter without oxygen, producing methane that can supply heat or electricity. Membrane filtration and additional treatment can prepare water for reuse, with the required quality depending on its intended purpose.

Nutrient recovery routes include fertiliser production and microalgae cultivation, which remove nutrients and produce harvestable biomass. Fibres, shells and empty fruit bunches offer routes to fuel, compost, biochar, cellulose and other materials, with ash finding uses in construction. POME's central role reflects these connections rather than dominance in every category, since fibre and shell combustion generally supplies more energy than recovered biogas in the configurations reviewed.

What AI Has Already Demonstrated

Existing applications include plantation monitoring, crop-yield forecasting, predictions of digester performance and models of membrane filtration. Most address individual processes, leaving operators without a continuously updated picture of how water, energy and waste decisions interact. The review distinguishes prediction, which estimates an outcome, from optimisation, which selects operating settings, and automatic control, which repeatedly adjusts equipment using measured feedback.

One study combining an industrial-data-trained model with simulation estimated biogas production of 12,683 cubic metres per day for an improved configuration. Its economic analysis projected a payback period of 5.89 years, around 29% shorter than conventional treatment using cooling ponds. These figures describe a particular modelled configuration and cannot establish expected returns for other mills or the proposed digital twin.

Research across four industrial digesters found that plants with identical designs required different combinations of loading, temperature and recirculation. Membrane studies demonstrated promising predictions of water flow and oil rejection, with field validation remaining an identified need. No study retrieved by the review demonstrated automatic feedback control on a palm oil effluent-treatment or resource-recovery unit, making fully connected operation an unproven step.

Recovering More Resources Requires Careful Choices

Producing cleaner reusable water can increase electricity demand, chemical consumption and operating costs. Burning empty fruit bunches provides energy, removing that material from potential uses such as cellulose products or biochar. The preferred route depends on the mill's fuel needs, available technology, product buyers, freshwater constraints and treatment requirements.

A comparison cited in the review reported climate impacts of 8.10 kilograms of carbon dioxide equivalent per tonne of treated effluent for biogas and 57.02 kilograms for biodiesel. Biogas performed better on environmental and net-energy measures, with biodiesel delivering greater total energy recovery and a higher economic surplus in that assessment. These results support decisions based on local priorities rather than a universal ranking of recovery technologies.

Recovering several products in sequence can capture more value from a single stream, and partnerships with paper, polymer, furniture or construction businesses can create additional outlets. The environmental accounting must include energy, chemicals, transport and residual waste to check whether an improvement moves pollution elsewhere. Separate measures of water reuse, energy return, nutrient recovery, material diversion and greenhouse gas emissions provide a clearer picture than an unexplained combined sustainability score.

A Digital Twin That Operators Could Put to the Test

The proposed digital twin would act as an updated digital representation of the mill, combining sensor readings, laboratory measurements and predictive models. Dynamic material flow analysis would track incoming, stored and outgoing resources, helping identify missing flows or losses. Life cycle assessment would evaluate environmental consequences during decision-making, with explicit assumptions about how impacts and recovered-product credits are assigned.

Recommendations would balance operating costs, emissions, freshwater use and recovered-resource value within equipment capacities and discharge limits. Predictions would include uncertainty ranges, allowing a digester-loading decision to account for a possible high organic load rather than relying on one expected value. Human approval, independent operating safeguards, audit records, cybersecurity measures and a return to previously safe settings would support deployment.

Reliable data remain a major barrier, and the authors propose collecting at least twelve consecutive months of measurements from three differently configured mills. Sensors, installation, maintenance and staff training would add costs, making staged trials essential for assessing whether the decision system earns its place. Proposed tests cover resource-balance accuracy, prediction reliability, environmental consistency, changing effluent conditions, computing speed and performance against existing operating practices.