Bench-Scale Adsorption of Methylene Blue Dye on Coconut-Shell Activated Carbon

Dye-Adsorption Study is a bench-scale wastewater treatment experiment built around one of India's real industrial problems: coloured effluent from textile dyeing. The team prepares activated carbon from waste coconut shells — pyrolysed and physically activated in a college muffle furnace — and measures how well it removes methylene blue, a standard cationic dye, from synthetic effluent in batch experiments. Methylene blue is the ideal teaching dye because its concentration maps directly to absorbance at 664 nm on the department spectrophotometer through a Beer-Lambert calibration.

The chemical engineering content is the modelling, not just the removal percentage. From equilibrium data at two temperatures the team fits the Langmuir and Freundlich isotherms (nonlinear least squares in Python, linearised regression in Scilab as cross-check) and reports adsorption capacity and the dimensionless separation factor. From time-course data they fit pseudo-first-order and pseudo-second-order kinetics and identify which mechanism governs uptake. Effects of adsorbent dose, contact time, initial concentration and pH complete the parameter map.

The Indian grounding is the point: India is a major coconut producer, coconut-shell activated carbon is a real domestic industry concentrated in Tamil Nadu and Kerala, and small dyeing units face discharge norms under the Environment (Protection) Rules that make colour removal a live compliance issue. The whole study runs on consumables under ₹3,000 plus instruments every chemical engineering department already owns, and produces fitted parameters that are genuinely the team's own — the evidence an examiner can probe line by line.

Colour is the most visible pollutant in textile dyeing effluent, and clusters of small dyeing units in Karnataka, Tamil Nadu and Telangana regularly face action over it: dyes absorb light, choke photosynthesis in receiving water bodies, and several classes are toxic or persistent. Conventional treatment at small units removes much of the organic load but leaves residual colour, and commercial activated carbon, while effective, is an ongoing cost that small units avoid.

Coconut shells are an abundant Indian agricultural residue and coconut-shell activated carbon is a mature domestic industry, yet students never measure for themselves how such carbon actually behaves: what its capacity is, how fast it adsorbs, which isotherm and kinetic models describe it, and how dose, pH and concentration change the answer. There is a need for a bench-scale, fully documented adsorption study that prepares carbon from waste coconut shells, measures its methylene-blue removal rigorously, fits the standard equilibrium and kinetic models with honest error analysis, and reports the parameters a treatment designer would actually use.

Abstract

Textile dyeing effluent is coloured, visible and regulated; dyeing clusters around Bengaluru and Tirupur live with this daily. This project prepares activated carbon from waste coconut shells by pyrolysis and post-activation in a college muffle furnace, then runs a full batch-adsorption study on methylene blue: a 664 nm Beer-Lambert calibration, dose and contact-time screening, equilibrium isotherms at two temperatures, and uptake kinetics. Langmuir and Freundlich isotherms and pseudo-first/second-order kinetic models are fitted in Python with Scilab cross-checks, and all results carry triplicate error bars. Consumables cost under ₹3,000.

Introduction

Adsorption on activated carbon is the workhorse of decolourisation, and coconut-shell carbon dominates the Indian market from an abundant agricultural residue. Making a modest-grade carbon in the department and measuring its actual performance connects three syllabus threads — mass transfer, reaction engineering and environmental compliance — around one measurable question: how much dye does a gram of our carbon take up, how fast, and according to which equilibrium and rate models?

Existing System

• Textbook chapters present isotherms with published constants; students never fit a model to their own equilibrium data.
• Commercial coconut-shell carbon treated as a black box teaches no preparation science.
• Small units rely on conventional treatment that leaves residual colour, which regulators flag.
• Limitations: no hands-on bridge between theory and measured behaviour and no feel for what a fitted constant is worth.

Proposed System

• Adsorbent preparation: wash, dry, crush and sieve waste coconut shells; pyrolyse at 500–600 °C in the muffle furnace; activate with a dilute chemical agent or steam; characterise by yield, iodine-style quick test, pH drift, moisture and ash content.
• Analytical base: a 664 nm calibration curve for methylene blue with R² ≥ 0.99, verified against known dilutions each bench day.
• Batch experiments: dose screening (0.2–2.0 g/L), contact time (5–180 min), initial concentration (10–100 mg/L), pH 3–11, and equilibrium at two temperatures in a thermostated shaker.
• Modelling: Langmuir (qmax, KL) and Freundlich (KF, n) fits on equilibrium data; pseudo-first-order and pseudo-second-order fits on kinetic data, compared by R² and residual plots.
• Reporting discipline: triplicate flasks, blank and no-carbon controls, mean ± standard deviation, and every figure regenerable from the raw absorbance workbook.

Feasibility Study

• Technical: pyrolysis is a routine muffle-furnace operation and the spectrophotometric assay is direct. The main risk, batch-to-batch carbon variation, is handled by preparing one large sieved batch before any kinetics begins.
• Economic: coconut shells are free or near-free from roadside vendors; chemicals (dye, acids/bases, filters) total roughly ₹2,800; no equipment purchase is needed.
• Operational: each campaign fits a 4-hour lab slot — about ten bench days over 14 weeks. Dye and carbon dust are handled with gloves, goggles, mask and fume hood; no human subjects or biological hazards are involved, and spent dye goes to the department's waste container.

In scope

• One adsorbent (self-made coconut-shell carbon), one model dye (methylene blue) in synthetic aqueous effluent, batch mode only.
• Characterisation limited to engineering-relevant basics: yield, moisture, ash, pH drift and a decolourising quick test (no BET surface-area analysis, which needs dedicated instrumentation).
• Isotherms at two temperatures, kinetic runs at one temperature, and dose/contact-time/pH screening.
• Analysis in Python with Scilab cross-checks; all raw absorbances retained in one workbook.

Out of scope (v1)

• Continuous-column (fixed-bed) experiments, breakthrough curves and scale-up design.
• Real textile effluent characterisation or treatment trials — the v1 matrix is synthetic, and real effluent is a listed extension.
• BET/BJH surface analysis, FTIR surface chemistry, SEM morphology or point-of-zero-charge instrumentation.
• Regeneration-and-reuse cycling beyond one preliminary desorption check, and treatment of other dye classes (reactive, azo) beyond a literature comparison.

The project uses an experimental research design with staged screening: preparation and characterisation first, analytical validation second, screening third, and the formal isotherm/kinetic campaigns last, each stage gated by the previous one's acceptance criteria.

Quality control: every bench day opens with a calibration verification and a blank (no carbon) flask; equilibrium is declared only when successive half-hourly readings change by less than 2%; the no-carbon control bounds any photodegradation or wall adsorption. Model selection is reported with R², residual plots and parameter confidence intervals, and the report states which model won and why — including the possibility that neither linearised form fits well, which is itself a defensible finding.

This is a laboratory study, so the architecture is the experiment-to-model data chain, drawn as the study design.

flowchart TD
  SH["Waste coconut shells (roadside vendors)"] --> PREP["Wash, dry, crush, sieve"]
  PREP --> PYRO["Pyrolysis 500-600 C, muffle furnace"]
  PYRO --> ACT["Post-wash, dry, store: characterised carbon batch"]
  ACT --> CHAR["Yield, moisture, ash, pH drift, quick decolourising test"]
  DYE["Methylene blue stock (analytical grade)"] --> CAL["Calibration at 664 nm, R2 >= 0.99, verified each bench day"]
  ACT --> BATCH["Batch flasks: dose, contact time, C0, pH, temperature, orbital shaker"]
  CAL --> BATCH
  BATCH --> FILT["Filter/centrifuge, read A664, triplicates + no-carbon control"]
  FILT --> WB["Raw absorbance workbook (one row per flask/time point)"]
  WB --> ISO["Langmuir and Freundlich fits, two temperatures"]
  WB --> KIN["Pseudo-first/second-order fits, residuals"]
  ISO --> RES["Results: capacity, separation factor, rate constants, optima"]
  KIN --> RES

Three decisions carry the design. First, one large sieved carbon batch is prepared before any adsorption begins, so batch-to-batch variation never contaminates the model fits. Second, the calibration is treated as a daily gate — every capacity number derives from it. Third, equilibrium models are fitted nonlinearly as the primary method with the familiar linearised plots shown as cross-checks, because linearisation reshuffles the error structure; the report presents both and states which it trusts and why.

• Fixed-bed column study: pack a small glass column, run breakthrough curves, and fit the Thomas or Yoon-Nelson models — the natural next step toward real treatment design.
• Real effluent extension: obtain (with permission and anonymisation) a composite sample from a local dyeing unit and test the carbon's performance on true matrix water.
• Commercial comparison: run the identical protocol on purchased coconut-shell carbon and report the capacity and cost per gram comparison.
• Regeneration cycling: test 3–5 adsorption–desorption cycles with mild chemical regeneration and track capacity fade.
• Other dye classes: repeat the core protocol on a reactive dye to contrast anionic vs cationic behaviour and pH sensitivity.