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FIG_001
Process flowsheet: feed, catalytic reactor, cooler and flash separator, with recycle compressor and purge.
FIG_002
Fixed-bed catalytic reactor, cut away, with the axial conversion and temperature profiles of a 1-D plug-flow model below it.
FIG_003
PEM electrolyzer stack, exploded: end plates, bipolar plates, membrane electrode assembly, H₂ and O₂ outlets.
FIG_004
One causal self-attention head: queries, keys and values, the softmax(QKᵀ/√d) weights with the causal mask, and one output as a weighted sum.
FIG_005
k-nearest-neighbour classification: three classes, the decision regions for k = 5, and one query’s vote.
FIG_006
Gradient descent on a quadratic loss surface: iterates θₖ₊₁ = θₖ − η∇L(θₖ) converging to the minimum.
FIG_007
Day-ahead forecast with a split-conformal interval: the band half-width is the calibrated quantile q̂ of past residuals.
FIG_008Sieve-tray distillation column, exploded: feed, trays and downcomers, reboiler below, condenser and reflux drum above.
FIG_009
Direct distillation sequence for a four-component feed: A | BCD, then B | CD, then C | D.
FIG_010
McCabe–Thiele construction for a binary column: equilibrium curve, q-line, rectifying and stripping lines, and the theoretical stages stepped off at R = 1.4·R_min.
FIG_011
U-tube shell-and-tube heat exchanger, sectioned: shell-side flow across the baffles, counter-current tube-side flow.
FIG_012
Feed-forward neural network: pixel input, two ReLU hidden layers, softmax output over class probabilities.
FIG_013
Superstructure optimization for a Power-to-X system: every candidate technology and link, with the selected design (y = 1) drawn solid.
FIG_014
Floating ball valve, DN50 class 150, half-sectioned; a quarter turn of the ball shuts the line.
Pareto front: cost against CO₂, solved by ε-constraintBi-objective design trade-off, both objectives minimised: total annualised cost c in M€/a (horizontal) against CO₂ emissions e in kt/a (vertical). The attainable set is bounded by the convex frontier e = -4 + 434/(c − 8.5) for c from 12 to 24. 11 efficient designs lie on it, evenly spaced along the drawn curve; 210 seeded candidate designs (small dots) lie above it inside the ideal–nadir box. A non-dominated filter over all 221 designs returns exactly the 11 efficient designs (round markers on the 2-unit Pareto front); every small dot is dominated by at least one of them. Axis ticks: cost 12 to 24 M€/a in steps of 3, CO₂ at 50, 75, 100 and 120 kt/a. Utopia point (12, 24) and nadir point (24, 120) span the dashed box. Dashed ε-lines show the ε-constraint method, min c subject to e ≤ ε, at ε = 89.27, 62.42, 43.18, 31.35 kt/a; each touches the front at its optimum, c = 13.15, 15.03, 17.7, 20.78 M€/a, where the constraint is active. The hatched quadrant is the region dominated by design A (15.03, 62.42). Inset: design A against design B (20.78, 31.35) as pixel bars, cost and CO₂, each metric on its own zero-based scale.12151821245075100120COST M€/aCO₂ kt/aABABABCOSTM€/aCO₂kt/a15.020.86231PARETO FRONTε-CONSTRAINTUTOPIANADIRDOMINATED
Cost versus emissions: candidate designs, the Pareto front, and solutions found with the ε-constraint method.
FIG_017
Model predictive control: the receding horizonTwo stacked time plots split by a vertical line at the current sample k. Upper plot: the measured output y of the first-order plant y[t+1] = 0.8 y[t] + 0.2 u[t], steady at 0.2 until the set-point steps to 1 at k-3, then rising; to the right of k, the dashed reference r = 1 and the predicted output y-hat(k+i|k) over a prediction horizon of Np = 10 samples, which is the model rolled out on the planned inputs. Lower plot: past inputs as zero-order-hold bars, then the plan from the quadratic cost sum (y-hat - r)^2 + 8 sum du^2 with control horizon Nc = 4: four shrinking upward moves, held constant afterwards. Only the first move, u(k), is applied; at the next sample the window shifts one step and the problem is solved again.yutPASTPREDICTION HORIZON NCONTROL HORIZON NNOW (k)REFERENCE rPREDICTED ŷAPPLIED u(k)
Model predictive control: the prediction and control horizons at time k, the predicted output, and the first planned move that is applied.
FIG_018
MILP: a relaxation, an integer cut, and a proofInteger program: maximize cᵀx = 4x₁ + 3x₂ over integer x₁, x₂ subject to x₁ ≥ 1, x₂ ≥ 1, x₁ + x₂ ≥ 4, 3x₁ + x₂ ≤ 19, x₁ + 3x₂ ≤ 19 and x₁ ≤ 5.5. The hatched hexagon is the LP relaxation, with vertices (1, 3), (1, 6), (4.75, 4.75), (5.5, 2.5), (5.5, 1) and (3, 1); its hollow optimum is the vertex (4.75, 4.75) with bound 33.25. Solid dots are the 22 feasible integer points, faint dots are infeasible ones; the ringed integer optimum is (5, 4) with value 32. The dashed Chvátal–Gomory cut x₁ + x₂ ≤ 9, the sum of the two ≤ 19 rows divided by 4 and rounded down, passes through (4, 5) and (5, 4), keeps every feasible integer point and removes the dithered wedge that holds the LP optimum. The faint dashed iso-objective line cᵀx = 33.25 touches the relaxation only at the LP optimum; the objective arrow c = (4, 3) stands perpendicular to it and points toward increasing cᵀx. Branch and bound on x₁ = 4.75: the child x₁ ≤ 4 has LP optimum (4, 5) with value 31, the child x₁ ≥ 5 has LP optimum (5, 4) with value 32; both are integral, so 32 is optimal and the gap to 33.25 is closed.x₁x₂cTxCUTx₁+x₂≤9FEASIBLEREGIONLP OPTIMUMINTEGEROPTIMUMBRANCH &BOUND33.25x₁ ≤ 4x₁ ≥ 531 (4,5)32 (5,4)
Mixed-integer program: integer lattice, LP relaxation, a valid cut and the branch-and-bound tree.
FIG_019
Two-stage stochastic program: scenario tree with recourseTwo-stage stochastic program min cᵀx + Σ p_s Q(x, ξ_s), drawn as a scenario tree. The first-stage decision x (installed capacity: PV 2.5 MW, wind 1.5 MW, battery 2 MWh, shown as pixel bars in a double-rimmed node) is a single node shared by every scenario, which is non-anticipativity. It fans out to four renewable-availability scenarios with probabilities sunny 0.35, windy 0.30, cloudy 0.25, calm 0.10 (sum 1.00). Each scenario card is a 24-hour sparkline of available renewable output (PV plus wind, MW) in 0.4 MW pixel steps, with the flat 1 MW load as a dotted line. The recourse y_s is the hourly dispatch: surplus charges the battery (90% efficient), deficits discharge it, and the rest is imported at 100 EUR/MWh. Q(x, ξ_s) is that import cost in thousand EUR per day (0.82, 0.23, 0.65, 1.73), and the probability-weighted bracket gives E[Q(x, ξ)] = 0.69.FIRST STAGE xPV2.5 MWWIND1.5 MWBATT2.0 MWh0.350.300.250.10Σ 1.00psSUNNYWINDYCLOUDYCALMLOAD 1.0 MWSCENARIOS ξs0.820.230.651.73RECOURSE ysQ(x,ξs)E[Q(x,ξ)]= 0.69 k€minxcTx+Σs ps Q(x,ξs)
Two-stage stochastic program: one first-stage design x, scenarios ξₛ with probabilities pₛ, recourse yₛ, and the expected recourse cost.
FIG_020
Compass: the construction of the KA monogramConstruction board of the KA monogram. The K's leg is a circular arc centred on the A's apex; it runs on through the A as its crossbar. The apex and the K's joint are the ends of a 3-4-5 right triangle, four strokes across along the cap line and three down the stem's axis, so the hypotenuse, five strokes, is the compass radius of the arc's centreline; the arc's inner and outer circles are 26 units apart. The arm leaves the joint along a 3:4 line, the mirror of the arc's start. Dotted axes, dashed cap line, baseline and centreline, solid primary construction and hollow anchor nodes at the apex, the joint, the cap-line corner, the arm's top, the A's inner apex, the crossbar on the axis and the outer corners.
Construction of the KA monogram: the K's leg is an arc centred on the A's apex that runs on as the crossbar; apex and joint close a 3-4-5 triangle whose hypotenuse is the compass radius.